{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-step forecasting: direct approach\n",
    "\n",
    "[Feature Engineering for Time Series Forecasting](https://www.trainindata.com/p/feature-engineering-for-forecasting)\n",
    "\n",
    "In the previous notebooks, we forecasted one step ahead; that is, the CO concentration for the next hour.\n",
    "\n",
    "In this notebook, we will predict the hourly pollutant concentration for the next 24 hours. That is, we will forecast 24 steps ahead (multi-step forecasting).\n",
    "\n",
    "We will carry out multi-step forecasting using a direct approach.\n",
    "\n",
    "## Data\n",
    "\n",
    "We will work with the Air Quality Dataset from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Air+Quality).\n",
    "\n",
    "For instructions on how to download, prepare, and store the dataset, refer to notebook number 3, in the folder \"01-Datasets\" from this repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from feature_engine.creation import CyclicalFeatures\n",
    "from feature_engine.datetime import DatetimeFeatures\n",
    "from feature_engine.imputation import DropMissingData\n",
    "from feature_engine.selection import DropFeatures\n",
    "from feature_engine.timeseries.forecasting import (\n",
    "    LagFeatures,\n",
    "    WindowFeatures,\n",
    ")\n",
    "\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Same function we saw in section 2.\n",
    "\n",
    "def load_data():\n",
    "\n",
    "    # Data lives here.\n",
    "    filename = \"../datasets/AirQualityUCI_ready.csv\"\n",
    "\n",
    "    # Load data: only the time variable and CO.\n",
    "    data = pd.read_csv(\n",
    "        filename,\n",
    "        usecols=[\"Date_Time\", \"CO_sensor\", \"RH\"],\n",
    "        parse_dates=[\"Date_Time\"],\n",
    "        index_col=[\"Date_Time\"],\n",
    "    )\n",
    "\n",
    "    # Sanity: sort index.\n",
    "    data.sort_index(inplace=True)\n",
    "\n",
    "    # Reduce data span.\n",
    "    data = data[\"2004-04-01\":\"2005-04-30\"]\n",
    "\n",
    "    # Remove outliers\n",
    "    data = data.loc[(data[\"CO_sensor\"] > 0)]\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO_sensor</th>\n",
       "      <th>RH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date_Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-04-04 00:00:00</th>\n",
       "      <td>1224.0</td>\n",
       "      <td>56.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-04 01:00:00</th>\n",
       "      <td>1215.0</td>\n",
       "      <td>59.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-04 02:00:00</th>\n",
       "      <td>1115.0</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-04 03:00:00</th>\n",
       "      <td>1124.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-04 04:00:00</th>\n",
       "      <td>1028.0</td>\n",
       "      <td>65.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     CO_sensor    RH\n",
       "Date_Time                           \n",
       "2004-04-04 00:00:00     1224.0  56.5\n",
       "2004-04-04 01:00:00     1215.0  59.2\n",
       "2004-04-04 02:00:00     1115.0  62.4\n",
       "2004-04-04 03:00:00     1124.0  65.0\n",
       "2004-04-04 04:00:00     1028.0  65.3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data.\n",
    "\n",
    "data = load_data()\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering steps\n",
    "\n",
    "The same pipeline from the previous notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Datetime features\n",
    "dtf = DatetimeFeatures(\n",
    "    # the datetime variable\n",
    "    variables=\"index\",\n",
    "    \n",
    "    # the features we want to create\n",
    "    features_to_extract=[\n",
    "        \"month\",\n",
    "        \"week\",\n",
    "        \"day_of_week\",\n",
    "        \"day_of_month\",\n",
    "        \"hour\",\n",
    "        \"weekend\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lag features.\n",
    "\n",
    "lagf = LagFeatures(\n",
    "    variables=[\"CO_sensor\", \"RH\"],  # the input variables\n",
    "    freq=[\"1H\", \"24H\"],  # move 1 hr and 24 hrs forward\n",
    "    missing_values=\"ignore\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Window features\n",
    "\n",
    "winf = WindowFeatures(\n",
    "    variables=[\"CO_sensor\", \"RH\"],  # the input variables\n",
    "    window=\"3H\",  # average of 3 previous hours\n",
    "    freq=\"1H\",  # move 1 hr forward\n",
    "    missing_values=\"ignore\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Periodic features\n",
    "\n",
    "cyclicf = CyclicalFeatures(\n",
    "    # The features we want to transform.\n",
    "    variables=[\"month\", \"hour\"],\n",
    "    # Whether to drop the original features.\n",
    "    drop_original=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop missing data\n",
    "imputer = DropMissingData()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop original time series\n",
    "\n",
    "drop_ts = DropFeatures(features_to_drop=[\"CO_sensor\", \"RH\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(\n",
    "    [\n",
    "        (\"datetime_features\", dtf),\n",
    "        (\"lagf\", lagf),\n",
    "        (\"winf\", winf),\n",
    "        (\"Periodic\", cyclicf),\n",
    "        (\"dropna\", imputer),\n",
    "        (\"drop_ts\", drop_ts),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split data into train and test\n",
    "\n",
    "We will leave the last month of data as hold-out sample to evaluate the performance of the model.\n",
    "\n",
    "Remember that we need data about the pollutant information at least 24 hours before the first forecasting point in the test set to create the input features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data.\n",
    "\n",
    "# input data\n",
    "X_train = data[data.index < \"2005-03-04\"]\n",
    "X_test = data[data.index >= pd.Timestamp(\"2005-03-04\") - pd.offsets.Hour(24)]\n",
    "\n",
    "# target\n",
    "y_train = data[data.index < \"2005-03-04\"][\"CO_sensor\"]\n",
    "y_test = data[data.index >= pd.Timestamp(\"2005-03-04\") - pd.offsets.Hour(24)][\n",
    "    \"CO_sensor\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare the target\n",
    "\n",
    "In the direct approach, we build a model to predict each one of the steps in the forecasting horizon.\n",
    "\n",
    "This means that we need to create suitable targets first:\n",
    "\n",
    "- 1 hour ahead\n",
    "- 2 hour ahead\n",
    "- 3 hour ahead\n",
    "\n",
    "...\n",
    "\n",
    "- 24 hour ahead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The forecasting horizon.\n",
    "horizon = 24\n",
    "\n",
    "# Create an empty dataframe for the new targets.\n",
    "y_train_multi = pd.DataFrame(index=y_train.index)\n",
    "y_test_multi = pd.DataFrame(index=y_test.index)\n",
    "\n",
    "# Add each one of the steps ahead.\n",
    "for h in range(horizon):\n",
    "    y_train_multi[f\"h_{h}\"] = y_train.shift(periods=-h, freq=\"H\")\n",
    "    y_test_multi[f\"h_{h}\"] = y_test.shift(periods=-h, freq=\"H\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>h_0</th>\n",
       "      <th>h_1</th>\n",
       "      <th>h_2</th>\n",
       "      <th>h_3</th>\n",
       "      <th>h_4</th>\n",
       "      <th>h_5</th>\n",
       "      <th>h_6</th>\n",
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       "      <th>h_8</th>\n",
       "      <th>h_9</th>\n",
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       "      <th>h_14</th>\n",
       "      <th>h_15</th>\n",
       "      <th>h_16</th>\n",
       "      <th>h_17</th>\n",
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       "      <th>h_20</th>\n",
       "      <th>h_21</th>\n",
       "      <th>h_22</th>\n",
       "      <th>h_23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date_Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-04-04 00:00:00</th>\n",
       "      <td>1224.0</td>\n",
       "      <td>1215.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1136.0</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>1345.0</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>1188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-04 01:00:00</th>\n",
       "      <td>1215.0</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>1345.0</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>1188.0</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-04 02:00:00</th>\n",
       "      <td>1115.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1345.0</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>1188.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-04 03:00:00</th>\n",
       "      <td>1124.0</td>\n",
       "      <td>1028.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>1188.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>911.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-04 04:00:00</th>\n",
       "      <td>1028.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>1275.0</td>\n",
       "      <td>1324.0</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>1188.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>911.0</td>\n",
       "      <td>873.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
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      ],
      "text/plain": [
       "                        h_0     h_1     h_2     h_3     h_4     h_5     h_6  \\\n",
       "Date_Time                                                                     \n",
       "2004-04-04 00:00:00  1224.0  1215.0  1115.0  1124.0  1028.0  1010.0  1074.0   \n",
       "2004-04-04 01:00:00  1215.0  1115.0  1124.0  1028.0  1010.0  1074.0  1034.0   \n",
       "2004-04-04 02:00:00  1115.0  1124.0  1028.0  1010.0  1074.0  1034.0  1130.0   \n",
       "2004-04-04 03:00:00  1124.0  1028.0  1010.0  1074.0  1034.0  1130.0  1275.0   \n",
       "2004-04-04 04:00:00  1028.0  1010.0  1074.0  1034.0  1130.0  1275.0  1324.0   \n",
       "\n",
       "                        h_7     h_8     h_9  ...    h_14    h_15    h_16  \\\n",
       "Date_Time                                    ...                           \n",
       "2004-04-04 00:00:00  1034.0  1130.0  1275.0  ...  1136.0  1296.0  1345.0   \n",
       "2004-04-04 01:00:00  1130.0  1275.0  1324.0  ...  1296.0  1345.0  1296.0   \n",
       "2004-04-04 02:00:00  1275.0  1324.0  1268.0  ...  1345.0  1296.0  1258.0   \n",
       "2004-04-04 03:00:00  1324.0  1268.0  1272.0  ...  1296.0  1258.0  1420.0   \n",
       "2004-04-04 04:00:00  1268.0  1272.0  1160.0  ...  1258.0  1420.0  1366.0   \n",
       "\n",
       "                       h_17    h_18    h_19    h_20    h_21    h_22    h_23  \n",
       "Date_Time                                                                    \n",
       "2004-04-04 00:00:00  1296.0  1258.0  1420.0  1366.0  1113.0  1196.0  1188.0  \n",
       "2004-04-04 01:00:00  1258.0  1420.0  1366.0  1113.0  1196.0  1188.0  1065.0  \n",
       "2004-04-04 02:00:00  1420.0  1366.0  1113.0  1196.0  1188.0  1065.0   999.0  \n",
       "2004-04-04 03:00:00  1366.0  1113.0  1196.0  1188.0  1065.0   999.0   911.0  \n",
       "2004-04-04 04:00:00  1113.0  1196.0  1188.0  1065.0   999.0   911.0   873.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Each column represents one of the targets.\n",
    "# Each target represents the number of steps ahead.\n",
    "\n",
    "y_train_multi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>h_0</th>\n",
       "      <th>h_1</th>\n",
       "      <th>h_2</th>\n",
       "      <th>h_3</th>\n",
       "      <th>h_4</th>\n",
       "      <th>h_5</th>\n",
       "      <th>h_6</th>\n",
       "      <th>h_7</th>\n",
       "      <th>h_8</th>\n",
       "      <th>h_9</th>\n",
       "      <th>...</th>\n",
       "      <th>h_14</th>\n",
       "      <th>h_15</th>\n",
       "      <th>h_16</th>\n",
       "      <th>h_17</th>\n",
       "      <th>h_18</th>\n",
       "      <th>h_19</th>\n",
       "      <th>h_20</th>\n",
       "      <th>h_21</th>\n",
       "      <th>h_22</th>\n",
       "      <th>h_23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date_Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-03-03 00:00:00</th>\n",
       "      <td>1047.0</td>\n",
       "      <td>1030.0</td>\n",
       "      <td>986.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>1108.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>1437.0</td>\n",
       "      <td>1473.0</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>1179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-03 01:00:00</th>\n",
       "      <td>1030.0</td>\n",
       "      <td>986.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>1111.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1108.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>1437.0</td>\n",
       "      <td>1473.0</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-03 02:00:00</th>\n",
       "      <td>986.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>1111.0</td>\n",
       "      <td>1165.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>1437.0</td>\n",
       "      <td>1473.0</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>929.0</td>\n",
       "      <td>951.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-03 03:00:00</th>\n",
       "      <td>992.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>1111.0</td>\n",
       "      <td>1165.0</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>1437.0</td>\n",
       "      <td>1473.0</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>929.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-03 04:00:00</th>\n",
       "      <td>1076.0</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>1111.0</td>\n",
       "      <td>1165.0</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1437.0</td>\n",
       "      <td>1473.0</td>\n",
       "      <td>1396.0</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>929.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>938.0</td>\n",
       "      <td>921.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        h_0     h_1     h_2     h_3     h_4     h_5     h_6  \\\n",
       "Date_Time                                                                     \n",
       "2005-03-03 00:00:00  1047.0  1030.0   986.0   992.0  1076.0  1104.0  1160.0   \n",
       "2005-03-03 01:00:00  1030.0   986.0   992.0  1076.0  1104.0  1160.0  1217.0   \n",
       "2005-03-03 02:00:00   986.0   992.0  1076.0  1104.0  1160.0  1217.0  1457.0   \n",
       "2005-03-03 03:00:00   992.0  1076.0  1104.0  1160.0  1217.0  1457.0  1337.0   \n",
       "2005-03-03 04:00:00  1076.0  1104.0  1160.0  1217.0  1457.0  1337.0  1111.0   \n",
       "\n",
       "                        h_7     h_8     h_9  ...    h_14    h_15    h_16  \\\n",
       "Date_Time                                    ...                           \n",
       "2005-03-03 00:00:00  1217.0  1457.0  1337.0  ...  1096.0  1108.0  1124.0   \n",
       "2005-03-03 01:00:00  1457.0  1337.0  1111.0  ...  1108.0  1124.0  1216.0   \n",
       "2005-03-03 02:00:00  1337.0  1111.0  1165.0  ...  1124.0  1216.0  1437.0   \n",
       "2005-03-03 03:00:00  1111.0  1165.0  1129.0  ...  1216.0  1437.0  1473.0   \n",
       "2005-03-03 04:00:00  1165.0  1129.0  1092.0  ...  1437.0  1473.0  1396.0   \n",
       "\n",
       "                       h_17    h_18    h_19    h_20    h_21    h_22    h_23  \n",
       "Date_Time                                                                    \n",
       "2005-03-03 00:00:00  1216.0  1437.0  1473.0  1396.0  1285.0  1206.0  1179.0  \n",
       "2005-03-03 01:00:00  1437.0  1473.0  1396.0  1285.0  1206.0  1179.0   929.0  \n",
       "2005-03-03 02:00:00  1473.0  1396.0  1285.0  1206.0  1179.0   929.0   951.0  \n",
       "2005-03-03 03:00:00  1396.0  1285.0  1206.0  1179.0   929.0   951.0   938.0  \n",
       "2005-03-03 04:00:00  1285.0  1206.0  1179.0   929.0   951.0   938.0   921.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Each column represents one of the targets.\n",
    "# Each target represents the number of steps ahead.\n",
    "\n",
    "y_test_multi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we create our target, we will add some missing data because we obviously don't have all the information that far ahead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h_0       0\n",
       "h_1      25\n",
       "h_2      46\n",
       "h_3      67\n",
       "h_4      87\n",
       "h_5     106\n",
       "h_6     124\n",
       "h_7     142\n",
       "h_8     160\n",
       "h_9     177\n",
       "h_10    193\n",
       "h_11    210\n",
       "h_12    227\n",
       "h_13    244\n",
       "h_14    261\n",
       "h_15    277\n",
       "h_16    293\n",
       "h_17    309\n",
       "h_18    325\n",
       "h_19    340\n",
       "h_20    355\n",
       "h_21    370\n",
       "h_22    385\n",
       "h_23    400\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_multi.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h_0      0\n",
       "h_1      3\n",
       "h_2      6\n",
       "h_3      9\n",
       "h_4     12\n",
       "h_5     15\n",
       "h_6     18\n",
       "h_7     21\n",
       "h_8     24\n",
       "h_9     27\n",
       "h_10    30\n",
       "h_11    33\n",
       "h_12    36\n",
       "h_13    39\n",
       "h_14    42\n",
       "h_15    45\n",
       "h_16    48\n",
       "h_17    51\n",
       "h_18    54\n",
       "h_19    57\n",
       "h_20    60\n",
       "h_21    63\n",
       "h_22    66\n",
       "h_23    69\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_multi.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's drop the missing data points in the\n",
    "# target and adjust our training and testing sets.\n",
    "\n",
    "y_train_multi.dropna(inplace=True)\n",
    "y_test_multi.dropna(inplace=True)\n",
    "\n",
    "X_train = X_train.loc[y_train_multi.index]\n",
    "X_test = X_test.loc[y_test_multi.index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train pipeline and model\n",
    "\n",
    "We train the engineering steps and the pipeline utilizing the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>week</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekend</th>\n",
       "      <th>CO_sensor_lag_1H</th>\n",
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       "      <th>CO_sensor_lag_24H</th>\n",
       "      <th>RH_lag_24H</th>\n",
       "      <th>CO_sensor_window_3H_mean</th>\n",
       "      <th>RH_window_3H_mean</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>month_cos</th>\n",
       "      <th>hour_sin</th>\n",
       "      <th>hour_cos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date_Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2004-04-05 00:00:00</th>\n",
       "      <td>4</td>\n",
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       "      <td>5</td>\n",
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       "      <td>1188.0</td>\n",
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       "      <td>1224.0</td>\n",
       "      <td>56.5</td>\n",
       "      <td>1165.666667</td>\n",
       "      <td>58.566667</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>2004-04-05 01:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>1065.0</td>\n",
       "      <td>65.8</td>\n",
       "      <td>1215.0</td>\n",
       "      <td>59.2</td>\n",
       "      <td>1149.666667</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>0.866025</td>\n",
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       "      <td>0.269797</td>\n",
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       "      <th>2004-04-05 02:00:00</th>\n",
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       "      <td>999.0</td>\n",
       "      <td>79.2</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>62.4</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>68.600000</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.519584</td>\n",
       "      <td>0.854419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-05 03:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>911.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>991.666667</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.730836</td>\n",
       "      <td>0.682553</td>\n",
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       "    <tr>\n",
       "      <th>2004-04-05 04:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>873.0</td>\n",
       "      <td>81.0</td>\n",
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       "      <td>65.3</td>\n",
       "      <td>927.666667</td>\n",
       "      <td>80.066667</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.887885</td>\n",
       "      <td>0.460065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     month  week  day_of_week  day_of_month  hour  weekend  \\\n",
       "Date_Time                                                                    \n",
       "2004-04-05 00:00:00      4    15            0             5     0        0   \n",
       "2004-04-05 01:00:00      4    15            0             5     1        0   \n",
       "2004-04-05 02:00:00      4    15            0             5     2        0   \n",
       "2004-04-05 03:00:00      4    15            0             5     3        0   \n",
       "2004-04-05 04:00:00      4    15            0             5     4        0   \n",
       "\n",
       "                     CO_sensor_lag_1H  RH_lag_1H  CO_sensor_lag_24H  \\\n",
       "Date_Time                                                             \n",
       "2004-04-05 00:00:00            1188.0       60.8             1224.0   \n",
       "2004-04-05 01:00:00            1065.0       65.8             1215.0   \n",
       "2004-04-05 02:00:00             999.0       79.2             1115.0   \n",
       "2004-04-05 03:00:00             911.0       80.0             1124.0   \n",
       "2004-04-05 04:00:00             873.0       81.0             1028.0   \n",
       "\n",
       "                     RH_lag_24H  CO_sensor_window_3H_mean  RH_window_3H_mean  \\\n",
       "Date_Time                                                                      \n",
       "2004-04-05 00:00:00        56.5               1165.666667          58.566667   \n",
       "2004-04-05 01:00:00        59.2               1149.666667          61.800000   \n",
       "2004-04-05 02:00:00        62.4               1084.000000          68.600000   \n",
       "2004-04-05 03:00:00        65.0                991.666667          75.000000   \n",
       "2004-04-05 04:00:00        65.3                927.666667          80.066667   \n",
       "\n",
       "                     month_sin  month_cos  hour_sin  hour_cos  \n",
       "Date_Time                                                      \n",
       "2004-04-05 00:00:00   0.866025       -0.5  0.000000  1.000000  \n",
       "2004-04-05 01:00:00   0.866025       -0.5  0.269797  0.962917  \n",
       "2004-04-05 02:00:00   0.866025       -0.5  0.519584  0.854419  \n",
       "2004-04-05 03:00:00   0.866025       -0.5  0.730836  0.682553  \n",
       "2004-04-05 04:00:00   0.866025       -0.5  0.887885  0.460065  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the pipeline and create features\n",
    "# for the train set:\n",
    "\n",
    "X_train_t = pipe.fit_transform(X_train)\n",
    "\n",
    "# Data with input features.\n",
    "X_train_t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6850,)\n",
      "(5769, 24)\n"
     ]
    }
   ],
   "source": [
    "# Our transformation pipeline removed observations\n",
    "# with missing data, so we need to remove those\n",
    "# observations from the target variable as well.\n",
    "\n",
    "print(y_train.shape)\n",
    "\n",
    "y_train_t = y_train_multi.loc[X_train_t.index]\n",
    "\n",
    "print(y_train_t.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultiOutputRegressor(estimator=Lasso(max_iter=50000, random_state=0))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultiOutputRegressor</label><div class=\"sk-toggleable__content\"><pre>MultiOutputRegressor(estimator=Lasso(max_iter=50000, random_state=0))</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(max_iter=50000, random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(max_iter=50000, random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultiOutputRegressor(estimator=Lasso(max_iter=50000, random_state=0))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lasso regression\n",
    "\n",
    "# Using the MultiOutputRegressor, we automatically create\n",
    "# one Lasso for each target.\n",
    "\n",
    "lasso = MultiOutputRegressor(Lasso(random_state=0, max_iter=50000))\n",
    "\n",
    "# Train the model\n",
    "\n",
    "lasso.fit(X_train_t, y_train_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5769, 24)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions\n",
    "\n",
    "preds = lasso.predict(X_train_t)\n",
    "\n",
    "# The predictions are made for each one of\n",
    "# the targets.\n",
    "\n",
    "preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>h_0</th>\n",
       "      <th>h_1</th>\n",
       "      <th>h_2</th>\n",
       "      <th>h_3</th>\n",
       "      <th>h_4</th>\n",
       "      <th>h_5</th>\n",
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       "      <th>h_7</th>\n",
       "      <th>h_8</th>\n",
       "      <th>h_9</th>\n",
       "      <th>...</th>\n",
       "      <th>h_14</th>\n",
       "      <th>h_15</th>\n",
       "      <th>h_16</th>\n",
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       "      <th>h_18</th>\n",
       "      <th>h_19</th>\n",
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       "      <th>h_21</th>\n",
       "      <th>h_22</th>\n",
       "      <th>h_23</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1014.221883</td>\n",
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       "      <td>1156.940596</td>\n",
       "      <td>1209.156707</td>\n",
       "      <td>1187.625872</td>\n",
       "      <td>1113.841140</td>\n",
       "      <td>...</td>\n",
       "      <td>1171.163151</td>\n",
       "      <td>1222.690845</td>\n",
       "      <td>1258.088085</td>\n",
       "      <td>1275.446127</td>\n",
       "      <td>1249.578876</td>\n",
       "      <td>1187.135133</td>\n",
       "      <td>1110.832260</td>\n",
       "      <td>1060.559396</td>\n",
       "      <td>1059.902412</td>\n",
       "      <td>1073.437260</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1033.061791</td>\n",
       "      <td>1005.752976</td>\n",
       "      <td>994.984547</td>\n",
       "      <td>1007.313915</td>\n",
       "      <td>1040.868570</td>\n",
       "      <td>1092.116576</td>\n",
       "      <td>1150.991837</td>\n",
       "      <td>1198.213017</td>\n",
       "      <td>1190.904396</td>\n",
       "      <td>1142.782125</td>\n",
       "      <td>...</td>\n",
       "      <td>1179.051657</td>\n",
       "      <td>1213.942910</td>\n",
       "      <td>1246.817845</td>\n",
       "      <td>1265.117799</td>\n",
       "      <td>1244.370161</td>\n",
       "      <td>1195.767090</td>\n",
       "      <td>1133.632159</td>\n",
       "      <td>1078.454911</td>\n",
       "      <td>1035.317076</td>\n",
       "      <td>999.426732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>986.427271</td>\n",
       "      <td>998.418563</td>\n",
       "      <td>1027.787380</td>\n",
       "      <td>1063.779519</td>\n",
       "      <td>1097.559490</td>\n",
       "      <td>1127.269756</td>\n",
       "      <td>1151.036258</td>\n",
       "      <td>1175.398223</td>\n",
       "      <td>1177.356265</td>\n",
       "      <td>1156.166341</td>\n",
       "      <td>...</td>\n",
       "      <td>1213.078293</td>\n",
       "      <td>1236.129038</td>\n",
       "      <td>1259.667941</td>\n",
       "      <td>1267.646224</td>\n",
       "      <td>1235.737029</td>\n",
       "      <td>1171.396209</td>\n",
       "      <td>1092.081020</td>\n",
       "      <td>1027.252833</td>\n",
       "      <td>973.287504</td>\n",
       "      <td>929.599374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>923.659002</td>\n",
       "      <td>953.160610</td>\n",
       "      <td>983.997455</td>\n",
       "      <td>1013.942609</td>\n",
       "      <td>1043.834950</td>\n",
       "      <td>1075.318512</td>\n",
       "      <td>1105.415924</td>\n",
       "      <td>1135.631447</td>\n",
       "      <td>1150.884676</td>\n",
       "      <td>1149.101759</td>\n",
       "      <td>...</td>\n",
       "      <td>1177.103683</td>\n",
       "      <td>1177.648294</td>\n",
       "      <td>1176.717498</td>\n",
       "      <td>1164.577476</td>\n",
       "      <td>1128.638147</td>\n",
       "      <td>1078.567197</td>\n",
       "      <td>1022.824337</td>\n",
       "      <td>972.873320</td>\n",
       "      <td>927.830151</td>\n",
       "      <td>899.502782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896.901441</td>\n",
       "      <td>935.173035</td>\n",
       "      <td>969.735974</td>\n",
       "      <td>998.731566</td>\n",
       "      <td>1025.928750</td>\n",
       "      <td>1055.121619</td>\n",
       "      <td>1084.698606</td>\n",
       "      <td>1113.331643</td>\n",
       "      <td>1136.144969</td>\n",
       "      <td>1148.864397</td>\n",
       "      <td>...</td>\n",
       "      <td>1145.480115</td>\n",
       "      <td>1128.390189</td>\n",
       "      <td>1106.499263</td>\n",
       "      <td>1078.811279</td>\n",
       "      <td>1042.035201</td>\n",
       "      <td>1003.797403</td>\n",
       "      <td>966.742024</td>\n",
       "      <td>931.722469</td>\n",
       "      <td>900.226307</td>\n",
       "      <td>886.962837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           h_0          h_1          h_2          h_3          h_4  \\\n",
       "0  1152.872436  1081.938802  1029.199658  1014.221883  1035.531882   \n",
       "1  1033.061791  1005.752976   994.984547  1007.313915  1040.868570   \n",
       "2   986.427271   998.418563  1027.787380  1063.779519  1097.559490   \n",
       "3   923.659002   953.160610   983.997455  1013.942609  1043.834950   \n",
       "4   896.901441   935.173035   969.735974   998.731566  1025.928750   \n",
       "\n",
       "           h_5          h_6          h_7          h_8          h_9  ...  \\\n",
       "0  1086.727042  1156.940596  1209.156707  1187.625872  1113.841140  ...   \n",
       "1  1092.116576  1150.991837  1198.213017  1190.904396  1142.782125  ...   \n",
       "2  1127.269756  1151.036258  1175.398223  1177.356265  1156.166341  ...   \n",
       "3  1075.318512  1105.415924  1135.631447  1150.884676  1149.101759  ...   \n",
       "4  1055.121619  1084.698606  1113.331643  1136.144969  1148.864397  ...   \n",
       "\n",
       "          h_14         h_15         h_16         h_17         h_18  \\\n",
       "0  1171.163151  1222.690845  1258.088085  1275.446127  1249.578876   \n",
       "1  1179.051657  1213.942910  1246.817845  1265.117799  1244.370161   \n",
       "2  1213.078293  1236.129038  1259.667941  1267.646224  1235.737029   \n",
       "3  1177.103683  1177.648294  1176.717498  1164.577476  1128.638147   \n",
       "4  1145.480115  1128.390189  1106.499263  1078.811279  1042.035201   \n",
       "\n",
       "          h_19         h_20         h_21         h_22         h_23  \n",
       "0  1187.135133  1110.832260  1060.559396  1059.902412  1073.437260  \n",
       "1  1195.767090  1133.632159  1078.454911  1035.317076   999.426732  \n",
       "2  1171.396209  1092.081020  1027.252833   973.287504   929.599374  \n",
       "3  1078.567197  1022.824337   972.873320   927.830151   899.502782  \n",
       "4  1003.797403   966.742024   931.722469   900.226307   886.962837  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's capture the predictions in a dataframe\n",
    "# to determine the RMSE and plot the results.\n",
    "\n",
    "preds = pd.DataFrame(preds, columns=y_train_t.columns)\n",
    "\n",
    "preds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for horizon 0 got rmse: 93.06984159410605\n",
      "for horizon 1 got rmse: 137.7071147109738\n",
      "for horizon 2 got rmse: 161.8366595186029\n",
      "for horizon 3 got rmse: 173.51942479499303\n",
      "for horizon 4 got rmse: 177.79595984826702\n",
      "for horizon 5 got rmse: 176.71573064553425\n",
      "for horizon 6 got rmse: 170.5539534238455\n",
      "for horizon 7 got rmse: 164.85136115957522\n",
      "for horizon 8 got rmse: 171.05824654779553\n",
      "for horizon 9 got rmse: 175.63551243070995\n",
      "for horizon 10 got rmse: 174.09252789886924\n",
      "for horizon 11 got rmse: 173.0303048783281\n",
      "for horizon 12 got rmse: 175.65484036858226\n",
      "for horizon 13 got rmse: 180.10117818062966\n",
      "for horizon 14 got rmse: 184.00434982545784\n",
      "for horizon 15 got rmse: 186.12551746706984\n",
      "for horizon 16 got rmse: 185.298737495326\n",
      "for horizon 17 got rmse: 182.36123729688885\n",
      "for horizon 18 got rmse: 181.44720027346978\n",
      "for horizon 19 got rmse: 181.2292592514873\n",
      "for horizon 20 got rmse: 176.69406874062366\n",
      "for horizon 21 got rmse: 171.0774974569061\n",
      "for horizon 22 got rmse: 170.95832399978192\n",
      "for horizon 23 got rmse: 170.7794317101948\n"
     ]
    }
   ],
   "source": [
    "# The RMSE in the train set.\n",
    "\n",
    "for h in range(horizon):\n",
    "    rmse = mean_squared_error(preds[f\"h_{h}\"], y_train_t[f\"h_{h}\"], squared=False)\n",
    "    print(f\"for horizon {h} got rmse: {rmse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the RMSE is worse for those values further into the future. This is normal; predictions about the furthest values in the future always have a greater uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jaloT2PLKogvQCdgqhDgERACbhBChQCYQWWXfCNO22rbbR/RQKM6DzE2NPsSTI7rRIcCTZ77aRlFphfVi02zjbC4s/T/4TwKs/QB6jYFHUmDUe9ZLFHB+2Q9NcwA2SxZSylQpZbCUMkpKGYVqUkqSUmYBi4E7TKOiBgD5UspjwM/AcCFEW1PH9nDTNvvofDkIp0Y3RQF4urnwzxviOZxbyJs/6+aoZqPwJPzyEvw7DlZNhR7XwEMbYPS0mkuNN5VPGLQJ0J3cmsMwrBlKCDEPuAxoJ4TIAF6QUs6sZfcfgKuANKAQuBNASnlSCPEysMG03z+klDV1mtuGZ4BaAjPtF7j82UYfZmCXQCYM7MhHqw4RE+bL2OTI+p+k2UdRHqx5H9ZOg9Iz0Gs0XDYZgrobe14hdCe35lAMSxZSynH1PB5V5WsJPFTLfrOAWVYNrimih8Hvb6hPmp4BjT7M81fHsD/nLM8uTCXUz4NLutZRiVSzn68fgD0/QM/rVJII6WW7c4fGmfpEys9fUU/T7EDP4G6o6GGAbPQQWjM3Fyc+uC2J6GBvHvhsE7uO6VEvDkdKtUpi0h1w86e2TRSgkkV5MeSm2fa8mlYDnSwaKrw3tGnbqNnc1fl6uPLRnX3xdnfhzo82cCy//rW+NRvKz1A1mkLj7XP+kFh1r5uiNAegk0VDOTlDlyGq36KyssmHC/Nrw6yJfTlTUs6dH22goFjXj3IY2aZKNeaRSbbWrhs4u+lObs0h6GTRGNHD4OzxP99Mmigm3JcPxiex7/gZHpyzibKKpichzQrMhfxs3fxk5uIGQT30lYXmEHSyaIwuQ9R9E4bQVje4WxCvj47jj30neH5Rql5j2BFkp0LbTuDuY78YQuN09VnNIehk0Rg+oeqf2IrJAuCmvpE8OrQrX6Zk8O4y3alpd1nbITTWvjGExqmr2AK9HopmXzpZNFb0MDiyTnWAWtHjw7oyJqk9by/dy4KNGVY9ttYAJWfg5AEIsVN/hZm5k1v3W2h2pgdvN1b0MFj5DhxcAT2vtdphhRC8MSaerPxiJn+1jTA/DwZFt7Pa8TULHd8JSIuuLCorJQdOnCU1M4+tR/JJzcxHSslL18USF+HXtDhCq4yIMlc+1jQ70MmisSL6gZuPaoqyYrIANQdj+u19GDttDfd/upEFD1xE91A7tpu3RuZO5ZDzk4WUkoxTRWzNyCM1I5+tGXlszzx9rux8G1dnYtv7cuRUMaM/WMUTw7tx3+AuODs1sgJtm7bgF6n7LTS708misVzcoPOlar6FlE0rR10D8xyM0R+sYuJH61n04CBC/Tyseg6tDtnbwd2PPLdQ1u/IYltGPtsy80nNyONUoRre7ObsRM8wH0b3bk9chB8JEf50CfLCxdmJvMJSnluUyps/7WH5nhzevjmR9v5tGheLLvuhOQCdLJoieijs/g5O7DWkVlC4v5qDcdP0Ndw5ewPz7x+It7v+ldlC0ZGtZDlHMfL1ZZSUV+LsJOgW4sPwmFDiI/2Ib+9P91Af3Fxq7vbz93Tj/VuTWLAxgxcX72Dkv1fw2ug4rk0Ib3gwIbGw9ycoKwLXRiYcTWsi/c7TFOY25LRfDCss1yvcjw9u68Ok2Rt4cM4mZk5IxtVZj0swQmWl5Pe9x/nojwNMz9rOKnkZY5IiuCGpPb3C/Wjj1rA1TIQQjE2OpF+nAP7y+RYembeZ33Yf56VRvfDxaMC63KFxICtVP0r7Pg38qTTNOvS7TlP4d4B23a0+hLa6S7sF8er1sazYm8Pzi1KpqNRzMKzpbEk5H68+xNC3lzNpdgpFx/fjJUoYfeUIXh8TR3JUQIMTRVUdA72Yf/9AHh3ala+3ZHLV1D/YeLgBxZP12haaA9DJoqmih8GhVVBaaOhpbunXgUeGRPNlSgY3Tl9N2vEzhp6vNThyspBXv9/JgNd/5YXFO/Bt48rUcb35fJQ3AF4dEq12LldnJ564ohvz71er3o2dvoa3l+6l3JLZ+v4d1WAK3cmt2ZFOFk0VPRQqSlR1UoM9cUU3/nNLIgdPnOWqqX8wY8V+fZXRQFJKNhw6yQOfbeTSKb8xa9UhLu0WxMIHL+KbhwZxXUI4Lsd3qEWugmOsfv4+HQP44dFLuL53e6b+uo+xH67hcO7Zup/k5KSG0OorC82O6k0WQogxQoh9Qoh8IcRpIUSBEELX0zbrOAhc2hjeFAWqDXxUYnuWPD6Yy7oF8doPu/VVhoXKKyr5enMm1723irHT17B6fy73XdqFP56+nPduTSKpQ9s/d87eDoFdDetM9vFw5e2bEnl3XG/2Hz/DVf/5g/kpR+ou8RISq9bjtkLxSk1rDEuuLN4ErpNS+kkpfaWUPlJKX6MDazZcPSAiGY6st9kpg308+PD2PuddZUxfvt+yJo1WpryikoWbMrjinRU89sUWCkvLeXV0LGueHcIzI3sQXtNwVhuV+bg2IZyfHhtMbHs/nlqwjYfnbuasab7GBULjoLQA8g4ZHpem1cSSZJEtpdxleCTNWXii+tRXXmqzU5qvMpY+filDugfzxo+7uWH6GvZlF9gsBkdWXlHJos0ZDH9nBU98uRV3Fyem39aHpY9fyvj+HfF0q2UgYFEe5KdfMBnPKOH+bZh7zwCeGdmDH7cf486PNpyb4HeeczO5db+FZh+WJIsUIcQXQohxpiapMUKIMYZH1pyEJap+ixzb59QgH3em3ZbEu+N6k557lqunruSD39Na7VVGRaU8lyQe/2Krmg1/Wx9+ePQSRsaG4lTfTGo7rGHh7CR44LIuTB3Xm43pp5g4a/2FCSM4RvWj6H4LzU4smWfhCxQCw6tsk8BCQyJqjsJ7q/ujWyAsweanF0JwbUI4A7sE8vevt/PmT3v4eXsWU8Ym0C2kdZQJqaiUfLv1KFN/3ceBE2fpEerD9NuSGB5jQYKo6twaFravNntNfDhOQvDIvM3cMXMdH0/q9+d8DNc2ajEknSw0O6k3WUgp77RFIM1aQGdw94Ojm6HPBLuF0c7bnWm39eH7bcf4+zfbuWbqSv4yrCv3De6MSwudyHcuSSzbx4GcJiQJs+xU8AxUZejt4Kq4MJwEPDx3M3fMWs/Hk/rha04YIbGq0rGm2YElo6EihBCLhBDHTbevhBARtgiu2RACwhPg2BZ7RwLA1fFhLHl8MFfEhDDl5z2MmbaapTuzW9QKfBWVkm+2ZHLFO8t57IstuDk7MW18kqm5KaxxiQLUlUVIrNVrfTXEyNgw3h+fRGpGPrfPXE9+kWmp3dA4yD8ChQ2Y0KdpVmLJx82PgMVAuOn2rWmbVlVYos07uevSztud98cn8f6tSWTlF3PPJykMfP1XXvluJ3uymm8neHpuIR/8nsYV7yznL59vwdXpzyRxZVwTkgRARTkc32W/NberGNErlGm39WHn0Xxun7mO/MKyPzu5s3fYNzitVbKkzyJISlk1OcwWQjxmUDzNV3hvqChV9XvCE+0dzTlXx4cxvFcIy/fksGBjBh+vOcT/Vh4kPsKPsX0iuDYhHH9PN3uHWafMvCK+33aU77YdY1uGWmyqdwd/PhifxMhejWxuqklumhqoYIf+ippcERPC9Nv68MBnmxg/cy1zbumBH6h+i06X2Ds8rZWxJFnkCiFuA+aZvh8H5BoXUjNlThBHNztUsgBVamJYTAjDYkLIPVPCN1uOMn9jBn//Zgcvf7eLK3qFMLZPBJd0DWr8ugtWlpVfzPepx/hu21E2p+cBEB/hx3NX9eCquDAi2npa/6TnRkI5RrIAGNozhA9v78N9n27k1nkH+dYzCKdsPXxWsz1LksUk4F3gHdQoqNWA7vSurm0n8DB1cjvwyxPo7c6kizsx6eJO7Diaz/yUDL7Zksn3244R4uvOmKQIbuwTQZcgb5vHdrygmB9Ts/hu21E2HDoFQEyYL0+P7M41ceF0CDQgQVSVlQpOrqo4pAO5vEcwM+7ow72fbmSTRySJR7fqctGazYk6Sww0U8nJyTIlJcX2J/74OijOg/tW2P7cTVBaXsmy3dnMT8ng9705VFRKkjr407dTAEHe7rTzdifIR92383ajradbk5p+issqyC8qI6+wjPyiMvZmF/D9tmOsO5hLpYTuIT5cHR/GNfFhdLZl0vrsBijIhgdW2u6cDbB8bw57P3uciU4/cvrxQwT6tY5h0ZrtCCE2SimTa3qs1g8oQoinpZRvCiHeRV1RnEdK+agVY2wZwnvDmvehvARc3O0djcXcXJwYGRvGyNgwjp8u5ustmSzclMlHKw9RWsMIKmcnQaCX2/lJxMeNIG933FycziUB831+Uel535eUX3jMzkFePDykK9fEh9lvbkjWduhyuX3ObYFLuwURcskQXFd9y/P/Xcgr999CO+/m83emNW91Xc2apyPb4SN6MxXeGyrL1GiV9kn2jqZRgn09uHdwF+4d3AUpJaeLy8kpKOHEGdOtoIScMyWcKCg9t21fdgEnzpSel1i83Jzx93TDt40r/m1c6dzOG39PV/zauOJnuvdv44ZfG1dC/TzoEuSFsONwVc6egDNZDtO5XZseiYNgFfid3s24GWuZe88Agnx0wtCUpTuzcXESXNY9yOr/T7UmCynlt6YvC6WU86s+JoQYa9UoWoqqndzNNFlUJYRQb+5tXIkOrrs5yJxYyioq8fVwrXW5UYdlnhntQJ3bNQroAi4e/KVrCUO2FzLuv2uZe09/gn30+uytnZSS13/chX8bVy7vEWz141vyH/2shds0/47Qpq3DTM6zJXNiaWdqimp2zCOMQuw/x6JOzi4QHEN48T5m39mPzFNF3Pa/dZw86xjzezT7WXvgJAdyznJr/46GHL/W/2ohxJWm/or2QoipVW6zgVrqKLdyQqjJeUc32zsSraGyUsEnDLwC7R1J/UJjIWs7AzoFMHNCModyC9XEPfNMb61Vmrs+HV8PF66JDzPk+HV9BDyK6q8oBjZWuS0GRhgSTUsQ3lvNAi4rtnckWkOYy3w0B6HxUHQSTh/louh2fHh7H/ZmFzDxoxqq1WqtwokzJfy0/Rg39InAw7Xx68XXpdZkIaXcKqX8GIiWUn5c5bZQSnnKkGhagvBEqCzXJRmak/ISOLHH8fsrzMxJzdR0dnn3YN4dl8S2jHzumr2BotIKOwan2cOCjRmUVUjG9+9g2DksaVyOEkIsEELsFEIcMN8Mi6i5M5crP6abopqNnD0qwTeXK4uQXuo+a9u5TSNjQ3n7pgTWHzrJvZ+mUFKuE0ZrUVkpmbc+nX5RAUQHGzfs3NJCgtNQ/RSXA58AnxkWUXPnFwltAnS/RXNihwWPmsTDV1UMqLa2xajE9vxzTDx/7DvBQ3M2t6gqw1rtVu/P5XBuIbcaeFUBliWLNlLKX1GzvQ9LKV8ErjY0quZMCHV1cXSLvSPRLJW1HVw81LDU5sLUyV3dTX0j+ceoXvyyK5vHvthCRWXLq9CgnW/u+sO09XRlZKyxa7BYkixKhBBOwD4hxMNCiNGA7QsHNSfnOrmL7B2JZonsVLVsqXMzqrgUGg8nD0DJmQseumNgFM9f1ZPvtx3jqQVbqdQJo8U6XlDMkh3Z3JBkXMe2mSXJ4i+AJ/Ao0Ae4DbDfcnDNQXgiyIoaP/lpDkZK9XtqLp3bZiGxgFQl8Wtwz+DOPHFFNxZuyuRv32ynJdaA02B+SgbllZJxBjdBQT3JQgjhDNwspTwjpcyQUt4ppbxBSrm2vgMLIWaZVtbbXmXbFCHEbiHENtPqe/5VHntWCJEmhNgjhBhRZftI07Y0IcTkxv2YNnauk3uLXcPQLFBwTA1DdfTJeNWZ+1eqdHJX98iQaB64rAtz16Xz8ne7dMJoYcwd2wM7B9qkSnSdyUJKWQFc3MhjzwZGVtu2FIiVUsYDezHNBBdCxAC3AL1Mz/lACOFsSlbvA1cCMcA4076Ozbc9eLbTndzNQZbjrWFhEb8IVRK/jqtXIQRPj+jOxIuimLXqIG8t2WvDADWjrdiXQ8apIsM7ts0saaTdLIRYDMwHzpo3SikX1vUkKeUKIURUtW1Lqny7FrjR9PUo4HMpZQlwUAiRBvQzPZYmpTwAIIT43LRvzdfejkJ3cjcf2aYRRebhqM2FEKrfotqIqAt3E7xwbQwl5ZW891saHq5OPDykq42C1Iw0d106gV5ujOhVpWM7a7uqeN3O+r9jS/osPFAr4w0BrjXdrrHCuScBP5q+bg8cqfJYhmlbbdsvIIS4VwiRIoRIycnJsUJ4TRSeCDm7oLTQ3pE4Lilh+RT4cLD9BgNkbQf/DupTenMTEqsmf1bWPadCCMGr18cypnd7/rVkL//7Q0+Tau6yTxfz6+7j3JgccX4ttqV/h4X3GnJOS64s/ielXFV1gxBiUFNOKoR4HjVvY05TjlOVlHIGMAPU4kfWOm6jhfcGWanG8Ef2q3//1kZK+O01WPGm+j5zE0Q16c+qcbJSm19/hVloHJQXqVFR9XySdHISvHljPMXlFbzy/S7auDkz3qCCc5rxvthwhIpKybi+1ZqgTqRBhwGGnNOSK4t3LdxmESHERNSVyXj5Z49bJhBZZbcI07batju+sER1r/stLiQl/PaqShS9xqhtR+odM2F9pYVwcn/z668ws6CTuyoXZyf+fXNvhvQI5u9fb+fnHVkGBqcZpaJS8vn6dC6ObkdUO68/HygrgvwjEBhtyHnrqjo7UAjxVyBICPFElduLQKMG9AohRgJPA9dJKau2zywGbhFCuAshOgFdgfXABqCrEKKTEMIN1Qm+uDHntjnfcPAK1smiunOJYgr0vh1umKnWvE63Q7I4vktd/TWXMh/VBXUHJ5cG1SFzc3HivVt7Ex/hz6PzNrPx8EkDA9SMsHzvcY7mF19YB+rkAUBCOxsnC8ANNfnOBfCpcjvNnx3TtRJCzAPWAN2FEBlCiLuA90zHWCqE2CKEmA4gpdwBfInquP4JeEhKWSGlLAceBn5Grdz3pWlfxyeE6rfQndx/khKWvaISRdIdcO1UcHJSl81H1kGljctTZDeTBY9q4+KuPkUe31X/vlV4urkwc0Iy4f5tuOvjFNKOXzixT3Ncc9amE+TjzrCYkPMfyE1T9wZdWdS1Ut5yYLkQYraU8nBDDyylHFfD5pl17P8q8GoN238Afmjo+R1CeG9I+wVKz4KbV/37t2RSwrKX4Y+3IGkCXPNvlShAJYtNH0PObgix4cjorO3g5g3+UbY7p7UFx0DmxgY/LdDbnY/v7MeYaauYMGs9ix68iGBfvdqeozuaV8Rve47zwGVdcHWu9lnfnCwMKltjSZ+FuxBihhBiiRBimflmSDQtjbmTu57hjS2elPDrP2pOFPBnh1z6GtvGlW1aw8LJkn8DBxUcA3mHoaSgwU/tEOjJRxP7caqwlIkfbaCgWC+e5Og+33AECdxSvWMbVOe2Tzi4GzNBz5L/kvnAZuBvwFNVblp9dCf3n4li5dvQZ+KFiQJUBVWvYNUUZcu4snc03yYoM/OVWM6eRj09LsKPD8YnsTe7gAc+20Rpua5U66jKKyr5YkM6g7sGERngeeEOuWkQaFwxTEuSRbmUcpqUcr2UcqP5ZlhELYlvGHiHtt5+Cynh15dMieJOuPqdmj/FC6GuLmx5ZZF3GEpON9/ObbPgnuq+CYttXdY9mNfHxLEy7QTPfLVNlwVxUMt2Hyf7dEntM7Zz9xkyGc/MkmTxrRDiQSFEmBAiwHwzLKKWJjyxdV5ZSAm/vAgr34HkSXD123U393QYCHnpcPqobeLLamZrWNTGPwpcPRvcyV3d2ORInhzejUWbM3nz58ZdpWjGmrs+nRBfd4b2CL7wwcKTUHTKsM5tsGxSnrnCbNWmJwl0tn44LVB4b9j7syolbVBbYo2kVB3rRafUfWC07UpwSwm/vACr/gPJd8FV/6q/X6BDf3WfvhZixxgfY/Z2QPz5yby5cnKCoB5wvOmDBB+6PJpj+cVM+30/ob4eTLgoqunxaVZx5GQhy/fm8MiQrrhU79gGw0dCgQXJQkrZybCztwZhiYBUE6c6XtS0Y0kJGSmmSqmnVLVU8ycK863wpNpedAoqSv98rk8YJIyDxPGGjcM+F+PS/4PVUy1PFKDqHLl62i5ZZKWq9t2WMEotJEZ9IGkiIQT/GBXL8YISXvx2ByG+7oyMDbNCgFpTfb4hHQHc0jey5h1O7FP39kwWQghP4Amgg5TyXiFEV6C7lPI7w6JqScIT1f3RLU1PFms/gJ+fO3+bszt4BkCbtmo518Au4NlXfd2mrXpMOMOuxbDq36r/IHIA9L4Nel0P7lZcs7d6orj6LdUfYQlnV2jfx3YzubO3/zkAobkL7gWbP4MzOeAd1KRDOTsJpt7Sm/H/W8ujn29hzt3u9I3Src72VFZRyZcpGVzePZhw/zY175SbpiZo+htXwsWSdomPgI2A+Z0uEzVCSicLS/iEqk/1Te23KC1U7f9Rl8CI1/5MEK6elr0h9x4PBVmw9XP1xrL4YfjxGZUwEserRGbpG7uZlOoq5+gWtXZH+ho4uAL63q2uKBp6vA4D4Y9/qWGg1kxi1RWfhlOHVMJsCcxNacd3gvelTT5cGzdnZk7oyw3TV3P3xyksuH8gXUMM/H1odfplZzY5BXV0bINKFm07GdrUbMmRu0gpbxZCjAOQUhYK0dB3gVYuvHfTk8XG2XA2B276FMLiG3cMn1C4+DEY9BfI2ACbP4Xti2DLHAjoDIm3QsKt4FdDYd/qieHoFvUznT2uHhdO0K4bXPYcXPp0wxMFqBFRslI1tXW5vHE/oyXMI4eaawHB6szl1Y/vhM5NTxYAbb3cTJP2VjNh1noWPjiIUD89ac8e5q5PJ9zPg8u619CxbZabZuhIKLAsWZQKIdqgOrURQnQBSgyNqqUJS4Q9P6pPtB6+DX9+WbHqLI66BDoObHo8QqhKuJH9YOQbsOtbdbWx7BVY9ip0GaKuRFw8zk8O5yWG7hA9VP1s4YlqVFFT2/8j+qpjp681OFk00wWPauMVBJ6BTRo+W5PIAE8+mtiXmz9cw8SP1vPl/QPx9XC16jm0uh3OPcsf+07w+LBuODvV8gGsshJy96v/RwNZkixeQNVrihRCzAEGARONDKrFCe/NuU7uqEYsPLj5UziTBWNmWD003Lwg4RZ1O3kQtsxVtwWT1OPCNNomephKCmGJ6k3WiI5hD1/1Kdno+RZZqeDhr1Y0bAmEUDO5mzh8tiax7f2Yfnsf7vxoAw9+tomPJ/Wr/U1Ls7p564/g7CS4ubaObVCVZitKINDOVxZSyqVCiE3AAEAAf5FSnjA0qpamaid3Q5NFeSms/DdE9odOg60cWDUBnWDI83DZZPXp3tlVTVpzq2G2qFE6DITNc6Ci3Lj21+zt6kqoJbWmBseoq8PKSquXL7mkaxCvXB/L5IWpzFp5kHsG61HztlBaXsn8lCMM7RFcdxOgDYbNggWT8oQQo1GzuL83jYAqF0Jcb2hULY13sPoU25h+i63z4HQGDG5kP0BjODmrhYgi+9k2UYBKimVn/6wIa22VFZC9s/lPxqsuJEa9bvnphhz+5r6RDI8JYcrPe9iT1fA6VFrD/bwji9yzpfWvsZ27X93bO1kAL0gp883fSCnzUE1TWkOE91Zt/w1RUa6Guob3Nrw90mF0MPXJpBtUJ+rkAbW6XHMv81FdsKlGVLYxy9MLIXhtTBw+Hi48/sUWXUPKBuauSyeibRsGd61nOHTuPnD3VR9KDWRJsqhpHxtNBW5BwhLV5WJxfr27npM6Xw3xtOVVhb35tQe/SOP6LbKa+RoWtak6fNYg7bzdeX1MHDuPnWbqr/sMO48G+3POsOZALuP6dcCpvj4icwFBg98jLEkWKUKIt4UQXUy3t1HzLrSGCO+t7o9ZtgQmlRVqzkFIHHS/0ri4HJF5MSQjCtplb1eTl4J6WP/Y9uTuA/4dDE0WAMN7hTK2TwQf/J7GxsOnDD1XazZ3XTouToKbkuvo2DbLTTO8cxssSxaPAKXAF6ZbCfCQkUG1SOc6uS3st9ixSP0RDH6y9VxVmHUYoOZ05DV4za36ZW1X80Fc3K1/bHsLjjGsGaqq/7s2hjC/Nvz1yy0UlpYbfr7WprisggUbMxgRG0qQTz1/p2VFkGfcuttV1ZsspJRnpZSTpZTJptuzUsqzhkfW0ni1U80rliSLykpY8S/16bfndcbH5mgizYshGVD6w7zgUUsUHKPar8tL69+3CXw8XHnrpgQOnyzktR+sP1y3tft+2zHyi8ouXGO7JicPAtLQdSzMLBkN1U2vlGclYQmWdXLv/g5ydsElTzbvVdwaK7gnuPtZP1kUnoTTmS2vv8IsOAYqy1XCMNiAzoHcNagTn61NZ/neHMPP15rMWXeYzkFeDOwcWP/O5t+1wbO3Qa+UZ1vhvdVonKK82veRElZMUevo2qL6qiNycobIvtZPFuaZ2y31ysK8ap4Bk/Nq8uSI7nQN9uap+VvJKzT2aqa12Hn0NJvS8xjfvyMWVVUyeN3tqvRKebZk7rc4trX2ffb+rGZ6X/JX9abZWnUYoK6uiqzYiXpuJFQLm2NhFthVdd5buexHbTxcnXnn5kROni3l79/Y5pwt3Zx1h3F3ceKGJAurC+TuV4VKbbBWjl4pz5bCTCOiauu3kBJWvKlGtcTfZLu4HJF5vsWR9dY7ZtZ2tda3wePR7cbFTSUMG11ZgCoH8tiwrny79SiLt9polcMW6kxJOV9vzuSa+HD8Pd0se9KJfTbp3AbLksUEVLPTatSQ2Y1AipFBtVhegSoR1NZvsX8ZZG6Ei59QpTZas/Ak9SnZmvMtslNbbn+FWUiMVVbNa4j7L+1C7w7+/P3r7WTlF9v03C3JN1syOVtawfgBFnRsm+WmOU6ykFJ2quGmi8M0VlhizVcW5r4K3/aqVHhr5+apXitrzeQ+kwPHd6sV+Vqy4J5qLfMS25XkcHF24u2bEiktr+Tpr7YhjZgf08JJKflsbToxYb70jvS37EnmVTFt0LkNlo2GchVCPCqEWGC6PSyEaOUfe5sgvLealV29Lf7QSvUpetBjLXMOQGN0GKCutMqtUBF/3XQ1UqilLHhUm2Dz2ha7bXraTu28eO6qHqzYm8Nn64ypT9WSbT6Sx65jpxk/oINlHdtgswKCZpY0Q00D+gAfmG59TNu0xqhagbaqFW+Cdwgk3W7riBxXhwGq9HJdAwIsUXwa1v8Xel5rs09hdnOu7IftO5xvG9CRS7q247Xvd3HwhJ6K1RBz1qbj5ebMqMQGlM13wGTRV0o5QUq5zHS7E+hrdGAtlnnd56r9Funr1HKkFz0KrrWssdsanZuc18R+i5RZUJIPFz/e9JgcnX9HcPWyaSe3mRCCKTcm4OoseOLLLZRX6GKDlsgrLOW7bUe5vnd7vN0bUHbvxD7D192uypJkUWFaHQ8AIURnoMK4kFo4zwD1y63ab7HiTbXSWfKd9ovLEXkHqfHjTZlvUVYMaz+AzpdB+ySrheawnJwguIfNhs9WF+rnwcvXx7I5PY8PVxywSwzNzYKNGZSUVzK+fwPf9G2w7nZVliSLp4DfhBC/CyGWA8uAvxobVgtXdU3uzI2Q9gsMfNiY1eeauw4DVbJobKfp1rlwJluNMGstDFo1z1KjEttzTXwY7yzdy/bMBlRZboWklMxdl05SB39iwhu45HLufps1QYFlo6F+BboCj6KKCnaXUv5mdGAtWniiGrFSeFLVgPLwh3732Dsqx9ShvxrxcaIRJSwqytXa5e37GL/KoCMJjoHCE3DmuN1CeOX6WAK83Hjiyy0Ul+mGiNqsOZDLgRNnG35VUVkJJ/dDOwdKFkKIh4A2UsptUsptgKcQ4kHjQ2vBzOXKN38Ke36AAQ+qEtPahc5NzmtEU9TOr9XIs4sfb12Ve8+V/TC+Am1t/D3dmDI2gb3ZZ3j5O/vF4ejmrEvHr40rV8eHNeyJpzOgvNixriyAe0yr4wEgpTwF6I/BTRGWoO6XvaJWuOp/n33jcWSB0ao/p6H9FlLCynegXXfofrUxsTkq8/BZG5Qrr8ul3YK4b3Bn5qxL51s9u/sCxwuK+Xl7FmP7RODh2sDSPuYrbRusY2FmSbJwFlUG/gohnAEL56JrNWrTVnVMVZRCv3uhjb+9I3JcQqhRUQ1NFvuWqsKBFz/W+ir3egeBZzu7XlmYPTmiO0kd/Hl2YSqH9HDa88xPyaC8UjLOklLk1dlo3e2qLPkv+gn4QggxVAgxFJhn2qY1RUSyGuI4QLfo1avDANU+25A2+JVvg28ExI01Li5HFhLjEMnC1dmJd29NwtlJ8NDcTbr/wqSiUnVsX9QlkC5BjSgCmJsGbj42rXNmSbJ4BjUC6gHT7VfgaSODahWGvwJ3L1X1orS6dWjgYkiH16i5GRc90nprbAXHqFnclfaf69Devw1vjU1gx9HTerEkkxV7c8jMK+K2AY2cI5G7T3Vu27AvzpLRUJVSyulSyhtNtw+llPrjQVP5hEJIL3tH0TyEJYCLh1qX2xIr31H9HEl3GBuXIwuOgbKzxixN2wjDYkK4++JOfLLmMD+kHrN3OHb32drDBPm4c0VMSOMOYMMCgmatrDFXa5Zc3NXwV0tmcmdth30/Q/8HVDHC1irY/iOiqnt6ZA8SIv15ZsE2Due23v6LjFOFLNtznJuTI3F1bsRbcFmxad1t25au0clCax4i+6saUaWFde+38h1w84Z+d9smLkcV3EPdO1CycHNx4r1xvRECHp67mZLy1tlA8cWGIwDc0i+ycQc4eQBbrbtdlUXJQgjhLYQwfikmTatNh4GqamxmHYs0njwAOxaqsilt2touNkfk7qPKyth5+Gx1kQGeTBmbQGpmPq//YNvKuI6grKKSzzccYUj3YCLaNvLK18YFBM3qTBamFfLSgcNAuhDisKUT8oQQs4QQx4UQ26tsCxBCLBVC7DPdtzVtF0KIqUKINCHENiFEUpXnTDDtv08IMaFxP6bW7EWaalfW1cm9+l1VWG3AQ7aJydHZuexHbUb0CuXOQVHMXn2In7Zn2Tscm1q6M5ucgpKGLXBUXa55joWDJAshxN+Aa4DLpJSBUsoA4HLgStNj9ZkNjKy2bTLwq5SyK2pU1WTT9itRJUW6AvdiKoFuWr71BaA/0A94wZxgtFamTVv15lfbTO6CbNg8Ry0c5dvA2bAtVUiMemMpL7V3JBd49sqexEf48fSCrRw5WU/TYgsyZ91h2vu34dJuTRjyasN1t6uq68ridmCMlPJc6UjT1zcB9Q4zkVKuAE5W2zwK+Nj09cfA9VW2fyKVtYC/ECIMGAEslVKeNM0cX8qFCUhrLToMUGtyV9bQ1r32fagsU2XeNSU4RjXdndhr70guoPovkpASHp67idJy+w/xNdqBnDOsSstlXL9InJ2aMOTVDiOhoO5kIaWUFyyoK6UsAhr7mw2RUprHzWUB5nFj7YEjVfbLMG2rbfsFhBD3CiFShBApOTk5jQxPc2iRA6Dk9IVNK0V5sGEWxFxv804/h3ZuRJTjNUUBdAj05M0b49makc8/f2r5/Rfz1qfj4iS4qW8jO7bNTuxzuGSRaZqxfR4hxBCgyQOlpVqo12qL9UopZ0gpk6WUyUFBQdY6rOZIzk3OqzaEdsP/oLSgdSxu1BCB0aoPxw6r5lnqyrgwJgzsyMyVB1m6M9ve4RimuKyC+RszGNErlGAfj8YfyLzutoMli0eBD4UQs4UQj5huHwMzgIcbeb5sU/MSpntz/YZMoGq6jTBtq2271hr5dwCf8PM7uUsLYe00iL4CwuLtF5sjcnGDdt0c9srC7LmrexLb3pcn528l41TL7L/4IfUYeYVljG9MHaiqzCOh7LA8cK3JQkq5A4gFVgBRptsKINb0WGMsBswjmiYA31TZfodpVNQAIN/UXPUzMFwI0dbUsT3ctE1rjYRQ61tUncm9+TO1doO+qqhZcE+HGz5bnbuLM++NS6KiUvLIvM2UtcDlWOesS6dzOy8GdmlieR87DZuFukdDRQN9pJSzpJR/Nd1mAn2qLrNax/PnAWuA7kKIDCHEXcAbwBVCiH3AMNP3AD8AB4A04L/AgwBSypPAy8AG0+0fpm1aa9VhIOQfgfwMqChTw2Uj+0PHi+wdmWMKjoH8dCg+be9I6hTVzos3bohjc3oeU37eY+9wrOrXXdlsPHyKW/t3QDS1llNummnd7SZeoTRCXYu3/ht4tobtp02PXVvXgaWU42p56IJ+EFP/RY2D46WUs4BZdZ1La0Ui+6v79LVqpE9+Olw1pXUtbtQQ5vpjObshsp9tz336mFoyOGGcRetEXxMfztoDucxYcYCItm24fUDHpr+52tmuY6d5dN5mYtv7Nnw1vJqc2Gdad9v2BTLr+g2GSClTq2+UUqYKIaKMC0nT6hASq8p5pK+BQyvVJ+duI+wdleMK7qnuj++0bbLY/xt8dbdqIiwvtnjZ4L9dHUPmqSL+75sdbDx8itdGx+HlXn+icUTHTxdz1+wN+Hi4MnNCX9q4NXCBo5rYeN3tqurq4Pav47E2Vo5D0yzj7AIRfdUEvJzdrW/J1Iby66CSq636LSor4fd/wqejwaud+l0tewXO5lr0dA9XZ2ZO6MuTw7vx7dajjHp/FfuyCwwO2vqKSiu4+5MU8orKmDkxmRDfJoyAMjOvu22n4eF1JYsUIcQFHweEEHcDdRTo0TSDdRgA5UWq9lGvMfaOxrE5OUFQD9sUFDx7AubcAL+/BvE3wT3L4NqpUFIAv71i8WGcnAQPD+nKZ3f3J6+wjOveW8WizRkGBm5dlZWSx7/YQmpmPlNv6U2vcD/rHNi87rYdRkJB3cniMeBOIcTvQoi3TLflwF3AX2wSnabVpOMgdT/oUYvawlu9kBjI3qHWJTdK+jqYfgkcWgXX/BtGfwhuXurc/e6BlI9U1eAGuKhLO3549GLiIvx4/IutPLswtVmstDdlyR5+2pHF81f1ZFhj16uoiR1HQkHdQ2ezpZQXAS8Bh0y3l6SUA6WUrav6l+ZYoi6GCd9Bn0n2jqR5CI5RE7kasiytpaSE1e/B7KvUuiN3L1VVf6s2DV42GTwD4MdnGpywgn09mHt3f+6/tAvz1qdzw7TVDr0WxpcpR5j2+37G9+/AXRd3su7BT5iTheNdWQAgpfxNSvmu6bbMFkFpWp2EgE6XqCYWrX5GLYRUlAdf3AZLnoduI+G+5WpVw+ratIWhL6hBCakLGnwaF2cnJl/Zg5kTksk4VcQ17650yGq1q/ef4LmFqVzStR0vXtfL+iO57LDudlX6v03TWjrz8FlrJoujW2DGpbD3Jxj+Ktz8GXjU0Tbf+zYIS4Slf4eSM4065dCeIXz3yMV0aufF/Z9t5JXvdjrMBL79OWd44LNNdGrnxfvjkxq3Al59ctNU57adBnToZKFpLZ1XO/AKsk6ykBJSZsHM4WpS5MQf4KKH638Dc3KGK9+EgmPwx1uNPn1kgCfz7x/IHQM78r+VB7llxlqO5Rc1+njWcOpsKZNmb8DFSTBrYl98PQyaA5G7z26d26CThaa1DsExTR8+W3IGFt4L3z2u+o3u+0OVX7FUh/4QfwuseU/NF2gkdxdn/jEqlnfH9Wb3sdNcPXUlK/bap9J0SXkF9326kWP5xcy4I5nIAIPWfT+37rZ9OrdBJwtNax2CY9S8lMpGNtuc2Af/HQKp8+Hy52H8AvBqRJ2jK14CZzf4+bnGxVHFtQnhLH7kYoK83Znw0XqeXZhK2nHbzcmQUvLswlTWHzrJlBvj6dPRwHXZzq27rZOFpmlGComBskLIO9Tw5+ZnwMfXQWEu3PE1XPp04wcX+ISq5+/9CfYuadwxqugS5M3XDw3i9gEd+WpTBsPeXsHtM9exbHc2lZUGDhUG3v8tjYWbMnl8WDdGJda4zI712HnYLOhkoWmtg3lEVEOboory4LMbofQM3PENdL6s6bH0f0C96f002SpLvrZxU81SayYP4akR3dmXfYZJs1MY8tbvfLTqIAXFZU2PuZrvth3lX0v2Mrp3ex4daoM38HPJwn6Le+lkoWmtQVAPdd+QtS3KiuHz8eqN6ubPIDTWOrG4uMHIf6rSFWs/sM4xgUBvdx66PJo/nrmcd8f1JtDbnZe+3cnA15fx4uIdHDxhnfkZm9JP8cSXW+kb1ZY3boizTbHD3DTTuts+xp+rFnr6q6a1Bu7eqjyKpavmVVbCovvg8Eq4YSZ0vtS68XQdBt2uhBVTIP5m8A2z2qFdnZ24NiGcaxPC2Xokj49XH2LOusPMXn2Iy7sHceegTlzStZ1Fb/LlFZWcOFPKsfwisvKLyTpdzPu/pRHq68GHtyfj7mKF4oCWsNO621XpZKFprUVIL8uuLKRUHdA7v4YrXoa4G42JZ8Sr8MEA+OVFGPOhIadIiPTn7ZsTmXxVD+auS+eztencMWs9XYK8+FvXw8R5nmJ/p/FkFZSSlV/Msfzic0khK7+Y4wXFVO/6CPF1Z9bEvgR4uRkSc41y06BnnatCGE4nC01rLYJjYO/PUF6iSnPUZs17sG6a6lu46BHj4gnsAgMfhpVvQ/Kkhg3DbaBgHw8eG9aNBy7rwg+px9i5bB6XbHwNF1HJiuVLeabsXspwwcfdhRA/D8L8PIgObkeYnwchvur7UD8PQn09CPBys+06G4Un1eACO5X5MNPJQtNai+CeICvUMNja+h9SF8CSv0HM9TDiNeNnC1/yV9j6Ofz4tKpS62Rss467izOj/fZzfeEUCoPi2BswiDF7P2BkR0HF2E/w8Qsw9PyNYp6TYudmKN3BrWmtRX1lPw4sh0X3q6q+oz+0Te0td28Y/jIc2wKbPzX+fBkbYd44REBnvCZ9Tcytr8P10/A8ugafudep1f0cTe4+dW/H2dugk4WmtR6B0eDkqsqVV5e1XRUFDIyGW+aAqxUW67FU7A3Q4SL49R9QdMq482TvVOtteAfB7YtUJVyAxFvh1i/h1EGYeQXkONga4HZcd7sqnSw0rbVwdoV23S7s5M47AnNuVCvq3bZAVYm1JSHgyn+qRPH7G8ac4+RBtXqfszvc/vWFo6+ih8LE71V/zszhcHiNMXE0Rm4atI2yy7rbVelkoWmtSXDP85uhCk/CZzdAaaFKFH4R9okrLB763Anr/2v9JWBPH4NPRkFFiZqBHlDLOhPhiWo9Dq92av+d31g3jsY6kWb3zm3QyULTWpeQGMg/AsWnTZPublXNL7fM+bNPw16G/E1NOvvxaeut6ld4Ul1RFObC+K9UsqxL2yiYtESty/HlBFhnzJBei9l53e2qdLLQtNYk2JQQsnfAwnvUgkSjp6vFpOzNM0AljEN/qAmBTahMC6i1vz+7QRXhGzcPIvpY9jyvQFXapMfVKnEt+XvjCzA21elMte62nUdCgU4Wmta6mD9Zf/MQ7FqshsfG3mDfmKpKnqTmXuz8Bt5Lhq/uaVyHc1kxzBun1v0eOxs6DW7Y89084aZPoO/dsHoqLLpX9WfYmoOMhAKdLDStdfHvoDqyT+5Xb8oDH7J3ROdzclYzu/+yTcW2+zt4vz/Mn1jzKK6aVJTBgjvVFcr106DHVY2P5ap/wdD/U6XZ59wIxfmNO1ZjOcgcC9DJQtNaFyHUhLvet6tSHo7KJwSGvwKPbYdLnoB9v8C0i1Rhw6Nban9eZaW6atrzg3qjT7i5aXEIoSYOXj8dDq+Gj66C00ebdsyGOLHPtO52iO3OWQshrdWR5ECSk5NlSkqKvcPQNM1aCk+qzuZ109Sn+64j1LoYEcl/7iMl/PAUbPiv6vsY/JR1Y0j7Fb68A9oEwINr1IRCo306RnXO37fc+HMBQoiNUsrkmh7TVxaapjk+zwC4/Fl4LFUlgowN8L+h8Mn16hM/wG+vqkQx8GG45EnrxxA9FG6ZC/npkPql9Y9fEweoNmumk4Wmac2Hh5+6YngsFa74B2Rvh4+uhA8uUuXOe9+umq+MqmnVaTCExMGGmdYb3lubsmLIS3eIzm3QyULTtObI3RsG/UV1hI98A4rzIG4sXPsfY4sfCgF9J6kkdWS9cecBNf/FzutuV6WThaZpzZebJwx4AB7fAWP+a3jVWgDiblKdzikzjT3PCdOwWQeYkAc6WWia1hIIYXw5dTN3b0i4BXYsgrMnjDvPuXW39ZWFpmla89T3Lqgohc2fGXeO3P3gHWrXdber0slC0zStoYJ7qnU/UmYZVwokd5/DdG6DThaapmmNkzwJ8g7D/l+tf+zyEsjZ7TD9FaCThaZpWuP0vA68gtQwWmtb/181+bDXaOsfu5F0stA0TWsMFzdIugP2/qTmQ1hL0Sk1ZyR6GHS+zHrHbSKdLDRN0xqrz0Q1CmvjbOsd84+31VXFsJesd0wr0MlC0zStsfw7qDpVmz6B8tKmHy8vXdXASrwVQmObfjwrskuyEEI8LoTYIYTYLoSYJ4TwEEJ0EkKsE0KkCSG+EEK4mfZ1N32fZno8yh4xa5qm1ajvXXA2R60P0lTLXlVXKpc/1/RjWZnNk4UQoj3wKJAspYwFnIFbgH8C70gpo4FTwF2mp9wFnDJtf8e0n6ZpmmPoMhT8O6phtE1xbCts+wL632+/tdDrYK9mKBegjRDCBfAEjgFDgAWmxz8Grjd9Pcr0PabHhwphq6mamqZp9XByUsNoD6+C7J2NP87SF6CNP1z8uNVCsyabJwspZSbwLyAdlSTygY1AnpSy3LRbBtDe9HV74IjpueWm/QOrH1cIca8QIkUIkZKTk2PsD6FpmlZV79vB2b3xVxdpv8KB32Dw0yphOCB7NEO1RV0tdALCAS9gZFOPK6WcIaVMllImBwUFNfVwmqZplvMKhF7Xw9bPoeRMw55bWamuKvw7qv4PB2WPZqhhwEEpZY6UsgxYCAwC/E3NUgARQKbp60wgEsD0uB+Qa9uQNU3T6pF8F5QWNHxhpNQvITtVrfXt4m5MbFZgj2SRDgwQQnia+h6GAjuB34AbTftMAL4xfb3Y9D2mx5fJlrgWrKZpzVtkv4YvjFRWDL++DOG9odcYY+NrInv0WaxDdVRvAlJNMcwAngGeEEKkofokzHPoZwKBpu1PAJNtHbOmaVq9GrMw0voP4XSGWvXPybGnvYmW+CE9OTlZpqSk2DsMTdNam5Iz8FYP6HEVjJlR976FJ+E/idChP4yfb5Pw6iOE2CilTK7pMcdOZZqmac1JQxZG+uMt1cfhYGU9aqOThaZpmjVZsjDSqUOwfoYq6xESY7PQmkInC03TNGuyZGGkZa+AcIbLHK+sR210stA0TbO2uhZGOroZUufDwAfBr/2FjzsonSw0TdOsrbaFkaSEpf8HnoEw6C/2ia2RdLLQNE2zNvPCSPt+Pn9hpLRf4eAKVdbDw89+8TWCThaapmlG6DNR3ZsXRqqsUFcVbaNUM1Uzo5OFpmmaEaovjLT1czi+A4a+oK48mhmdLDRN04xiXhgp9Us1Aio8CXqNtndUjeJS/y6apmlao5gXRvruCagogRv+q8qCNEP6ykLTNM0o5oWRKkqg25UQdbG9I2o0fWWhaZpmpD4T1JKpQ/5m70iaRCcLTdM0I7VpC2M/sncUTaaboTRN07R66WShaZqm1UsnC03TNK1eOllomqZp9dLJQtM0TauXThaapmlavXSy0DRN0+qlk4WmaZpWLyGltHcMVieEyAEON+EQ7YB6VltvFfTroOjXQdGvg9KSX4eOUsqgmh5okcmiqYQQKVLKZHvHYW/6dVD066Do10Fpra+DbobSNE3T6qWThaZpmlYvnSxqNsPeATgI/Too+nVQ9OugtMrXQfdZaJqmafXSVxaapmlavXSy0DRN0+qlk4WmaZpWr1aZLIQQUUKI7RbuGyCEWCqE2Ge6b2t0fLbSwNfhRSFEphBii+l2ldHxGamBP/tYIcQOIUSlECK52mPPCiHShBB7hBAjjInWONZ4HUzHKKrytzHduIiN0cDXYYoQYrcQYpsQYpEQwr/KY83676EurTJZNNBk4FcpZVfgV9P3rdU7UspE0+0HewdjQ9uBMcCKqhuFEDHALUAvYCTwgRDC2fbh2UyNr4PJ/ip/G/fbOC5bWwrESinjgb3As9Dy/x5ac7JwFkL81/RJaYkQok0t+40CPjZ9/TFwvU2isx1LX4eWyKKfXUq5S0q5p4aHRgGfSylLpJQHgTSgn5EBG6Spr0NLYenrsERKWW76di0QYfq6pfw91Kg1J4uuwPtSyl5AHnBDLfuFSCmPmb7OAkJsEJstWfo6ADxsuvSe1UKa4xrys9ekPXCkyvcZpm3NTVNfB4BOQojNQojlQohLrBqd7TTmdZgE/Gj6uqX8PdSoNSeLg1LKLaavNwJR9T1BqkkpLW1iiqWvwzSgC5AIHAPeMjowG2jw30AL1dTX4RjQQUrZG3gCmCuE8LVeeDbToNdBCPE8UA7MMTYsx9Cak0VJla8rAJda9ssWQoQBmO6PGx2YjVn0Okgps6WUFVLKSuC/tIzLa0v/BmqTCURW+T7CtK25adLrYGp2yTV9vRHYD3SzXng2Y/HrIISYCFwDjJd/zmxuKX8PNWrNycJSi4EJpq8nAN/YMRa7MSdMk9Gozs7WbjFwixDCXQjRCdWMsd7OMdmcECLI3JErhOiMeh0O2Dcq4wghRgJPA9dJKQurPNSi/x4a+kmqNXoD+FIIcRdqjYyb7ByPvbwphEhENcMdAu6zazQ2JIQYDbwLBAHfCyG2SClHSCl3CCG+BHaimiMeklJW2DNWI9X2OgCDgX8IIcqASuB+KeVJO4ZqtPcAd2CpEAJgrZTy/pb+96BrQ2mapmn10s1QmqZpWr10M5SJEOJ9YFC1zf+RUn5kj3jspTW/Dq35Z69Kvw6Kfh3Op5uhNE3TtHrpZihN0zStXjpZaJqmafXSyULTNE2rl04WmqZpWr3+H0H8oK+6X7RAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the first ten multistep forecasts\n",
    "# in the train set.\n",
    "\n",
    "# Each row corresponds to 24 hr forecasts.\n",
    "\n",
    "for i in range(0, 10):\n",
    "    tmp = pd.concat(\n",
    "        [\n",
    "            preds.iloc[i].T,\n",
    "            y_train_t.iloc[i].T,\n",
    "        ],\n",
    "        axis=1,\n",
    "    )\n",
    "\n",
    "    tmp.columns = [\"predicted\", \"actual\"]\n",
    "\n",
    "    tmp.plot()\n",
    "    plt.ylabel(\"CO concentration\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model managed to get the trend of the forecast, but deviates from the real values quite a bit.\n",
    "\n",
    "Hopefully, by the end of the course we can improve this model with more useful features :)\n",
    "\n",
    "# Forecast 24 hours ahead in test set\n",
    "\n",
    "We will evaluate the performance of the model in our test set now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>week</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>hour</th>\n",
       "      <th>weekend</th>\n",
       "      <th>CO_sensor_lag_1H</th>\n",
       "      <th>RH_lag_1H</th>\n",
       "      <th>CO_sensor_lag_24H</th>\n",
       "      <th>RH_lag_24H</th>\n",
       "      <th>CO_sensor_window_3H_mean</th>\n",
       "      <th>RH_window_3H_mean</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>month_cos</th>\n",
       "      <th>hour_sin</th>\n",
       "      <th>hour_cos</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date_Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-03-04 00:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>41.7</td>\n",
       "      <td>1223.333333</td>\n",
       "      <td>82.700000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-14 00:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1207.0</td>\n",
       "      <td>55.6</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>1201.333333</td>\n",
       "      <td>53.100000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-14 01:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>55.9</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1157.000000</td>\n",
       "      <td>54.533333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>0.269797</td>\n",
       "      <td>0.962917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-14 02:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>62.1</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>53.4</td>\n",
       "      <td>1130.333333</td>\n",
       "      <td>57.866667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>0.519584</td>\n",
       "      <td>0.854419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-14 03:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>68.9</td>\n",
       "      <td>940.0</td>\n",
       "      <td>51.8</td>\n",
       "      <td>1101.000000</td>\n",
       "      <td>62.300000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.123234e-17</td>\n",
       "      <td>0.730836</td>\n",
       "      <td>0.682553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     month  week  day_of_week  day_of_month  hour  weekend  \\\n",
       "Date_Time                                                                    \n",
       "2005-03-04 00:00:00      3     9            4             4     0        0   \n",
       "2005-03-14 00:00:00      3    11            0            14     0        0   \n",
       "2005-03-14 01:00:00      3    11            0            14     1        0   \n",
       "2005-03-14 02:00:00      3    11            0            14     2        0   \n",
       "2005-03-14 03:00:00      3    11            0            14     3        0   \n",
       "\n",
       "                     CO_sensor_lag_1H  RH_lag_1H  CO_sensor_lag_24H  \\\n",
       "Date_Time                                                             \n",
       "2005-03-04 00:00:00            1179.0       82.0             1047.0   \n",
       "2005-03-14 00:00:00            1207.0       55.6             1017.0   \n",
       "2005-03-14 01:00:00            1056.0       55.9             1050.0   \n",
       "2005-03-14 02:00:00            1128.0       62.1             1023.0   \n",
       "2005-03-14 03:00:00            1119.0       68.9              940.0   \n",
       "\n",
       "                     RH_lag_24H  CO_sensor_window_3H_mean  RH_window_3H_mean  \\\n",
       "Date_Time                                                                      \n",
       "2005-03-04 00:00:00        41.7               1223.333333          82.700000   \n",
       "2005-03-14 00:00:00        55.4               1201.333333          53.100000   \n",
       "2005-03-14 01:00:00        56.0               1157.000000          54.533333   \n",
       "2005-03-14 02:00:00        53.4               1130.333333          57.866667   \n",
       "2005-03-14 03:00:00        51.8               1101.000000          62.300000   \n",
       "\n",
       "                     month_sin     month_cos  hour_sin  hour_cos  \n",
       "Date_Time                                                         \n",
       "2005-03-04 00:00:00        1.0  6.123234e-17  0.000000  1.000000  \n",
       "2005-03-14 00:00:00        1.0  6.123234e-17  0.000000  1.000000  \n",
       "2005-03-14 01:00:00        1.0  6.123234e-17  0.269797  0.962917  \n",
       "2005-03-14 02:00:00        1.0  6.123234e-17  0.519584  0.854419  \n",
       "2005-03-14 03:00:00        1.0  6.123234e-17  0.730836  0.682553  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the input features:\n",
    "\n",
    "X_test_t = pipe.transform(X_test)\n",
    "\n",
    "X_test_t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426, 24)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions\n",
    "\n",
    "preds = lasso.predict(X_test_t)\n",
    "\n",
    "preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(498, 24)\n",
      "(426, 24)\n"
     ]
    }
   ],
   "source": [
    "print(y_test_multi.shape)\n",
    "\n",
    "# Adjust the target, it has observations\n",
    "# that were removed from the test set due\n",
    "# to null values.\n",
    "\n",
    "y_test_t = y_test_multi.loc[X_test_t.index]\n",
    "\n",
    "print(y_test_t.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1148.897601</td>\n",
       "      <td>...</td>\n",
       "      <td>1168.340448</td>\n",
       "      <td>1204.836350</td>\n",
       "      <td>1241.919172</td>\n",
       "      <td>1267.950247</td>\n",
       "      <td>1261.061914</td>\n",
       "      <td>1229.752736</td>\n",
       "      <td>1182.709742</td>\n",
       "      <td>1131.411304</td>\n",
       "      <td>1079.700521</td>\n",
       "      <td>1026.413671</td>\n",
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      ],
      "text/plain": [
       "           h_0          h_1          h_2          h_3          h_4  \\\n",
       "0  1122.867321  1062.455291  1032.853059  1040.337108  1074.203578   \n",
       "1  1141.110779  1064.348042  1025.341192  1027.799335  1060.734918   \n",
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       "3  1091.097136  1051.404482  1034.178890  1041.014532  1067.464152   \n",
       "4  1092.345968  1069.849258  1069.493358  1084.536425  1107.909874   \n",
       "\n",
       "           h_5          h_6          h_7          h_8          h_9  ...  \\\n",
       "0  1135.978334  1217.991003  1274.954502  1248.148873  1160.467624  ...   \n",
       "1  1115.861969  1185.090781  1233.090141  1208.189613  1133.941962  ...   \n",
       "2  1087.754891  1156.083885  1206.949166  1198.004144  1148.897601  ...   \n",
       "3  1106.399362  1151.895619  1191.331737  1195.122065  1175.943585  ...   \n",
       "4  1132.924974  1158.009182  1184.358108  1196.409132  1198.943318  ...   \n",
       "\n",
       "          h_14         h_15         h_16         h_17         h_18  \\\n",
       "0  1151.019588  1201.242335  1234.625491  1255.133049  1235.501115   \n",
       "1  1180.865113  1236.786773  1280.156083  1308.891837  1296.820311   \n",
       "2  1168.340448  1204.836350  1241.919172  1267.950247  1261.061914   \n",
       "3  1210.698631  1225.916504  1235.799002  1232.746713  1207.058246   \n",
       "4  1224.539996  1223.737991  1218.686655  1202.358490  1170.375230   \n",
       "\n",
       "          h_19         h_20         h_21         h_22         h_23  \n",
       "0  1187.870324  1125.787999  1068.568355  1034.323715  1006.808840  \n",
       "1  1245.681729  1175.977451  1125.091503  1111.759979  1100.308906  \n",
       "2  1229.752736  1182.709742  1131.411304  1079.700521  1026.413671  \n",
       "3  1161.171784  1106.813024  1068.441319  1052.699314  1042.960197  \n",
       "4  1121.826430  1067.769622  1034.485616  1023.508146  1018.814488  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's capture the predictions in a dataframe\n",
    "# to determine the RMSE and plot the results.\n",
    "\n",
    "preds = pd.DataFrame(preds, columns=y_test_t.columns)\n",
    "\n",
    "preds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for horizon 0 got rmse: 94.31943307476463\n",
      "for horizon 1 got rmse: 148.0523854519299\n",
      "for horizon 2 got rmse: 179.71012640539763\n",
      "for horizon 3 got rmse: 194.2567244958742\n",
      "for horizon 4 got rmse: 198.97489118077038\n",
      "for horizon 5 got rmse: 194.5416601549116\n",
      "for horizon 6 got rmse: 179.00838534421615\n",
      "for horizon 7 got rmse: 159.24817610006747\n",
      "for horizon 8 got rmse: 152.56815433965448\n",
      "for horizon 9 got rmse: 160.63281939426716\n",
      "for horizon 10 got rmse: 156.96198378337112\n",
      "for horizon 11 got rmse: 152.18193776924593\n",
      "for horizon 12 got rmse: 154.8569654674162\n",
      "for horizon 13 got rmse: 164.2711571975581\n",
      "for horizon 14 got rmse: 176.19075215315283\n",
      "for horizon 15 got rmse: 185.59534851518117\n",
      "for horizon 16 got rmse: 188.46066258216885\n",
      "for horizon 17 got rmse: 185.1042095045053\n",
      "for horizon 18 got rmse: 176.78907249038522\n",
      "for horizon 19 got rmse: 168.35414394312284\n",
      "for horizon 20 got rmse: 156.20499462551655\n",
      "for horizon 21 got rmse: 138.84323281930483\n",
      "for horizon 22 got rmse: 136.56404677162263\n",
      "for horizon 23 got rmse: 135.61259425033043\n"
     ]
    }
   ],
   "source": [
    "# The RMSE in the test set.\n",
    "\n",
    "for h in range(horizon):\n",
    "    rmse = mean_squared_error(preds[f\"h_{h}\"], y_test_t[f\"h_{h}\"], squared=False)\n",
    "    print(f\"for horizon {h} got rmse: {rmse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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p1cdyzAMQkW7AeKC75T1vi4iviPgCbwGjgW7ABMu5BkPVHNmpt5g6V7MFBSbXwpvISoagiN8rCruKqA5Qkg+nvLeqtcPEQim1HMi18fSxwGylVLFSag+QDgy0HOlKqd1KqRJgtuVcg6FqrCGzCSOqPycyTj8av4XnczBZrypc5dy20gjCZ13hs5gkIpst21TW1No2wIFK52RYxqob/wMicpeIrBORdaZnRSMmbSE07w4Rbas/x6wsvIOyYu3cdrW/An4XCy92cjtbLN4BOgJ9gCzgZXtNrJSaopRKUkolxcTE2GtagydRlAf7V/4xa/tsQmO0A9wUFPRsDm+DilLX+ytAfwERX69eWTi1+ZFS6rD1ZxF5H/je8jQTqPxVMNYyRg3jBsOZ7PpZR8ZUFzJrRcQSEWXEwqM5aMncbt3XtXaArkkV2d6rE/OcurIQkVaVnl4FWCOl5gLjRSRQROKBBGANsBZIEJF4EQlAO8HnOtNmgwexc4HO5I0dUPu5Riw8H3dxblvx8vBZh60sRGQWcAHQTEQygCeBC0SkD6CAvcDdAEqpbSLyObAdKAPuV0qVW+aZBCwAfIGpSqltjrLZ4MFUVEDaIuh0sU6Sqo3I9pCxxvF2GRyHuzi3rUR1hANrdPisu9hkRxwmFkqpCVUMf1jD+c8Cz1YxPg+YZ0fTDN7IwQ1QcKT2LSgrEe20j6PwOARHONIygyOwOred2UK1NqI6QPEJOHUEmnif39RkcBu8g50LQHzObHRUExGmVLlH407ObSvRln7cXroVZcTC4B2kLYDYgRASZdv5JnzWs8lK1o/uEDZrJcq7q88asTB4PieyIGtT7SGzlTGJeZ7NwWTt3Lb+O7oDXh4+a8TC4PmkLdSPZzc6qongSAhoYlYWnkpWMrTq7V6OZF9/LRhemphnxMLg+aQthPBYaF6HsmEi2m9hEvM8j7JiOLzdvfwVVrw4fNaIhcGzKSuGXUuqbnRUG6ZUuWeSvV07t93JX2EluqMWCy+sPmvEwuDZ7PsNSk/ZHjJbGWsTJC/8j+3VuEtZ8qqwhs8WHHW1JXbHiIXBs9m5APyCdP+KuhLRDkpOQuEx+9tlcBxZyRDUFCLjXW3JH4ny3vDZWsVCRMaJSJqI5InICRHJF5ETzjDOYKgRpbRYxA+DgJC6v9+aa3Fsr13NMjiYgxtd23O7Jry4+qwtK4sXgDFKqaZKqXClVJhSKtzRhhkMtXI0HY7tqbl3RU2YXAvPw52d2+DV4bO2iMVhpdQOh1tiMNQVa6Ojmrri1YQRC8/DnZ3bAH4BupeKFybm2VIbap2IfAZ8AxRbB5VSXzvKKIPBJtIWQEzi7x/6dSU4Qu99m8Q8z8GdndtWvDR81haxCAcKgMprfQUYsTC4jqI82Lei4YXkTPisZ+HOzm0rUR1h8+deV322VrFQSt3mDEMMhjqxa4ltjY5qI6I9HEmzj00Gx3Mw2f0yt88mqgMU50FBLoRGu9oau2FLNFSsiMwRkWzL8ZWIxDrDOIOhWtIW6m+YbQc1bJ6I9nplYXIt3J+yEu2zcFd/hRUvrT5ri4P7I3R3utaW4zvLmMHgGioqtFjY2uioJiLbQ1khnMqxj20Gx5G9HcpL3NtfAV5bfdYWsYhRSn2klCqzHNMA7+vsYfAcDm/VH+71DZmtjImI8hzcsSx5VUS0171VGuHK4qiI3CQivpbjJsD7ctkNnsNhS2fdNv0bPpdJzPMcDiZDYNPfv7m7K34B0LSt1yXm2SIWfwKuAw4BWcA1gHF6G1xHTgr4+NsnIsasLDyHrGRo7ebObSvWgoJehC3RUPuAMU6wxWCwjZxUaJbQcH8FQGATCIk2uRbuTlmJXlEOusfVlthGVAfY8oVXhc9W+79NRB5TSr0gIm+g8yrOQCn1oEMtMxiqIyfFvk5Ok2vh/niKc9tKVAedC1R4zPZWv25OTV/NrCU+1jnDEIPBJkoLtX+h93j7zRnRXjvNDe6Lpzi3rVirzx7d5f1ioZT6zvJjgVLqi8qvici1DrXKYKiOI2mAgpgu9pszoh2kztMhuT6mar9b4inObSunw2d3Q9sBrrXFTtjyP+MJG8fOQESmWpL4/vCVTUQeFhElIs0sz0VEJotIuohsFpF+lc69xVIiPU1EbrHBXoM3k5OqH2O62m/OiHZ6i+PkYfvNabAvWcnQqpfn7P9Hel/4bE0+i9HApUAbEZlc6aVwoMyGuacBbwLTz5q3LbrOVOVN4tFAguUYBLwDDBKRKOBJIAntN1kvInOVUqZbTWMlJ0WXgLYu8+1BZJx+PL4PwlvZb16DfTjt3L7b1ZbYjl8gNI31qsS8mlYWB9H+iiJgfaVjLlBrQR6l1HIgt4qXXgUe40yn+VhgutKsAiJEpJXlOouUUrkWgVgEjKr1tzJ4LzkpOizRL8B+c5rwWfcmZ4de+XmKv8JKlHeFz9bks9gEbBKRmUqpUntcTETGAplKqU1y5nKyDXCg0vMMy1h141XNfRdwF0C7dvUsWW1wf3JSoHmifee0isUxEz7rlpwuS97XpWbUmagOsPUrV1thN2zxWcSJyJcisl1EdluPul5IREKAvwH/qrOVNqCUmqKUSlJKJcXEmGokXklZsf6mZk9/BYB/MIQ2N7kW7kpWMgSGu3dZ8qqI6gBFx3X1WS/A1kKC76D9FBeifRCf1uNaHYF49GplLxALbBCRlkAm0LbSubGWserGDY2Ro+mgKuwvFqAdkkYs3BNrWXJPi1Tzsuqzttz9YKXUT4AopfYppZ4CLqvrhZRSW5RSzZVScUqpOPSWUj+l1CG0H2SiJSpqMJCnlMoCFgAjRCRSRCLRjvEFdb22wUvISdGPjhALk5jnnpSXaue2pyTjVaZy+KwXYItYFIuID5AmIpNE5CqgSW1vEpFZwEqgi4hkiMjtNZw+D9gNpAPvA/cBKKVygWeAtZbjacuYoTGSk6rDEaM72X/uiPaQlwEV5faf21B/sndAebHnObfBEmUnXiMWthTX+T8gBHgQ/cF9IVBrvoNSakItr8dV+lkBVfbHVEpNBabaYKfB28lJ0fvW/kH2nzuine68d+IgRLSt/XyDc7Bmbnuacxss4bPeU322RrEQEV/geqXUI8BJTLVZgyvJSXXMFhRonwXorSgjFu7DwY2e6dy2Et3Ba1YWNW5DKaXKgXOdZIvBUD3lpdrBbc8yH5Wx9rUwTm73wlOd21aiOnhNYp4t21AbRWQu8AVwyjqolPraYVYZDGeTu1tvEzlqZdE0FhDj5HYnrM7tgXe62pL6E9VRV54tyPX4goK2iEUQujPeRZXGFGDEwuA8TkdCOWhl4RcIYa1MYp47YXVue6K/wsrpiKg9jUIsPlBK/VZ5QESGOsgeg6FqclIBgWadHXcNEz7rXnhaWfKqqBw+G2uHNsAuxJaNwDdsHDMYHEdOiv4wDwhx3DVMYp57cTAZAsI8pyx5VXhR+GxNVWeHAOcAMSLyl0ovhQO+jjbMYDgDR0ZCWYlop1thlpeCr79jr2WonaxknYznqc5t0GHeXlJ9tqZ/hQB08p0fEFbpOAFc43jTDAYL5WW66ZGj/BVWItrrciJ5GY69jqF2ykvh0FYdCeXpRHlH+GxNVWeXActEZJpSyqzNDa7j+D7t6HTGygK03yLKQ+P6vYWcFM93bluJ6gDbv3W1FQ3GFgd3oIhMAeIqn6+UuqjadxgM9sSRNaEqE2lyLdwGa1lyT3ZuW4nuCIW5OoQ2ONLV1tQbW8TiC+Bd4APAFM4xOJ/TYuHASCiA8Da69pSJiHI9Wcme79y2Ujkiqo3nRkTZIhZlSql3HG6JwVAdOakQHguBYY69jq+/vo4RC9dzcKNnZ25Xxroiztzg0WJhy7/EdyJyn4i0EpEo6+FwywwGKzkpjnduW4loZxLzXI3Vue2JZcmrIrojNOvi8X4LW8TiFuBRYAW/9+Fe50ijDIbTVFRAzk7H+yusRLY3KwtXY3Vue4O/wkq3MbDvNzh1xNWW1JtaxUIpFV/F4QUbiQaPIG8/lBU6d2WRn6VbuBpcw+me231caYV96TZWh2WnfO9qS+pNrWIhIiEi8g9LRBQikiAilzveNIMBS5kPnLeyiGgPKJNr4UpOO7c7utoS+9Gihy6zvn2uqy2pN7b24C5BZ3OD7oH9H4dZZDBUxlmRUFasuRbH9jrneoY/cjAZWvXyDue2FRG9FbVnmQ6h9UBs+dfoqJR6ASgFUEoVAOJQqwwGKzmp0KSl8+LTKyfmGZxPeRkc3uodyXhn022sLrOfOt/VltQLW8SiRESC0WXJEZGOgNnQNTgHZ0ZCAYS3Bh8/k5jnKtZ+AGVF0N4LC1u37qfbrHroVpQtYvEk8CPQVkRmAD8BjznUKoMBQCnnFBCsjI+vLvxmVhbO50gaLH4SEkZCl9Gutsb+iEDiGNj1ExSdcLU1dcaWaKhFwDjgVmAWkKSUWupYswwG4EQmlJx07soCtJPb5Fo4l/IymHM3+AfDmMn6g9Ub6TYGyksgbaGrLakztkRDXYXO4v5BKfU9UCYiVzrcMoPBWTWhzsY0QXI+v70KmevhspchrKWrrXEcsQO1D277N662pM7YtA2llMqzPlFKHUdvTRkMjiXbIhbNE5173cj2cCobSgude93GStZmWPo8dB9HZuylPDV3G1OW7yI9Ox+llKutsy8+PpB4BaQthpJTrramTtgiFlWdU2tNKRGZKiLZIrK10tgzIrJZRJJFZKGItLaMi4hMFpF0y+v9Kr3nFhFJsxy32PJLGbyEnBQIjXF+7+IIa/VZs7pwOGXFMOduVEgU38U+zKhXlzNj9T7+Oy+Fi19ZznkvLOGf32zl55TDFJZ4SR3TbmN0omn6YldbUidsKSS4TkReAd6yPL8fXfKjNqYBbwLTK429qJT6J4CIPAj8C7gHGA0kWI5BwDvAIEsNqieBJHQ01noRmauU8sxAZUPdcLZz20plsXC2v6SxseS/kL2dt1r+l5e+3ceAuEhevrYPfr7C0tQclqRm89WGDD5ZtY8APx+GdIjmwi4xXNi1Oe2jQ11tff1odw6ENNO1orqNdbU1NmOLWDwA/BP4zPJ8EVowakQptVxE4s4aqxwCEIolHBcYC0xXes25SkQiRKQVcAGwSCmVCyAii4BRaEe7wZuxRkL1utb51zaJec5h/2rUisl8I8OZfKADT4zuzB3ndcDXRzu3bxjUjhsGtaO4rJy1e46xJDWbJanZPPXddp76bjsdmoVyQZfmXNg1hoHxUQT6eUi3Z18/6HoZbP0KSot061UPoFaxUEqdAh631wVF5FlgIpAHXGgZbgMcqHRahmWsuvGq5r0LuAugXbt29jLX4CryD0FxnmtWFk1agG+g2YZyIPknjlP86W0UlkfzSdTdzB0/hK4tw6s8N9DPl3MTmnFuQjP+eXk39h09dXrVMWP1Pqb+toeQAF8eurgzdw7zkLJ13cbCho9h18/Q9VJXW2MTtvgeOgOPYKdOeUqpvwN/F5EngEnYyVmulJoCTAFISkryMq9YI+R0JJQLtoF8fCCirUnMcxCrdx8lc8Z9XFl2kM96vM2scRfXaVXQPjqUW84J5ZZz4igsKWfV7qNMX7mXZ+ftIDYymNE9WznQejsRPwyCImDHXO8RCxzXKW8GMA8tFplA20qvxVrGMtFbUZXHl9rRBoO74uwCgmcTYUqV25ui0nJeXphKym9z+STgRw73uJ0J197QoDmDA3y5sGtzzukUzfgpq3j4i03ENQslsVXVqxS3wdcfulwKKT9AWQn4BbjaolqxJRqqTCn1jlJqjVJqvfWoz8VEJKHS07GA5esjc4GJlqiowUCeUioLWACMEJFIEYkERljGDN5OToquBxUa45rrmyZIdmVrZh5j3vyVz37ZypuhH1AR3ZkWVz5rt/kD/Xx576b+hAX5cef0deSeKrHb3A6j21i91bpnuastsQmHdcoTkVnASqCLiGSIyO3AcyKyVUQ2oz/4/89y+jxgN5AOvA/cB2BxbD8DrLUcT1ud3QYvxxoJ5apM3oh2UJgLxfmuub6XUFZewVtL0rnq7d84XlDKwq4/0LQsF59x7+lsbTvSPDyIKTcnkZ1fzH0z1lNaXmHX+e1Oxwt1KfYdntFBz5ZtKGtuw6OVxhRQoydJKTWhiuEPqzlXUU2ElVJqKjC1djMNXoNSkLPDtWGFkZXCZ1t0d50dHsyhvCLunbGejfuPc1mvVjyfuI8m334D5/8V2vSr9f31oXfbCJ6/uicPfbaJZ77fztNjezjkOnbBLxC6jIId38Nlr+ooKTfGlmioeGcYYjCc5tQRXfPfVf4KODPXwohFncnOL+KG91eRnV/M6+P7MLZTALx9LbTqDcMerX2CBnBV31h2ZOUzZfluEluFM2GgG0dHJo6BLV/olqsdzne1NTViSzSUP3AvMMwytBR4TylV6kC7DI0ZV0ZCWTFZ3PUm91QJN3+whqy8Ij65fSBJ7SPhs5v0lt5V72nnroP566iupB7K51/fbqVT8yYMiHNyFQBb6XQx+IfoBD03FwtbfBbvAP2Bty1Hf8uYweAYXFVAsDKhzfR/YuPkrhN5haVMnLqavUdP8eEtSSTFRcGm2br39EX/cFqdL18fYfL4vsRGhnDvp+vJPO6mdb4CQiDhEn1/Kty7nIktYjFAKXWLUupny3EbMMDRhhkaMTmpEBgOYS6MlxexVJ81YmErJ4vLuPWjNaQeyufdm/tzTqdmupf5/Md0iYshtRZ+sCtNQ/x5f2ISxaUV3DV9nfvWlkocAycPw4HVrrakRmwRi3JLdzwARKQD9s23MBjOxNodz9U9DYxY2ExhSTl3fLyWzRl5vDGhHxd2aQ4VFfDt/fob85Vv68ZSTqZT8yZMntCX7VknePTLTe5ZxbbzSF0xwM076NkiFo8CS0RkqYgsA34GHnasWYZGTU6qexTwM4l5NlFcVs7dn65n9Z5cXrmuN6N6WPpRbJ8Du5fCyP9AlOviZC7s2pzHRnbl+81ZvLNsl8vsqJbAMOg0XGdzV7hvuK8t0VA/WZLprP97U5VSpge3wTEU5OpeEq70V1iJaAdFeVB4HIIjXG2NW1JaXsGkmRtZvjOHF67uxdg+lUq3bfgEmraDfre6zD4r95zfgR1ZJ3hxQSpdWoQxPLGFq006k25jIXUeHNwAsUmutqZKbOmUdz8QrJTarJTaDISIyH2ON83QKHF1mY/KRJqIqJoor1D8+bNkFm0/zNNju3PdgEoVe/Iy9aqi93hda8vFiAjPX92L7q3D+b/ZyaRnu1myZedR4OOvo6LcFFv+Fe+0dMcDwNJL4k6HWWRo3LhD2KwVa6ly47f4AxUVise+3MwPm7P4+6WJTBwSd+YJmz8DFPSpKjfXNQQH+DLl5iSC/H254+N15BW4UfR/cIQOnd3+rU5KdUNsEQtfkd89jSLiC7h/1SuDZ5KTCv6hEB7raktMrkU1KKX457db+WpDBn+5pIqy4EpB8kxoNwSi3KtkeOuIYN69qR+ZxwuZNGsDZe5UEqTbWP3F5NBmV1tSJbaIxY/AZyIyXESGoxsP/ehYswyNlpwUiOnsFlsXBEfq2j1GLE6jlOI/P+xgxur93HtBRx64qNMfT8pcD0fToLf7rCoqkxQXxTNje/BL2hFeXJDqanN+p8tlIL5uGxVly//Iv6IjoO61HD8BjznSKEMjxlWtVKtCRPstTMe807y8cCcf/rqHW8+J47GRXZCqwpuTZ4JfMHS/0un22cr4gboL35RfdrM547irzdGERkPcuW67FVWrWCilKpRS7yqlrrEc7ymlTJ6Fwf4U5UH+QffwV1hp2RMOrHH77Fpn8ObPaby5JJ0JA9vy5BXdqhaK0iLY+iUkXg5BTZ1vZB14fHRXokMD+de326iocJMP525j9KrM6rtzI9xgrW8wWMjZqR/dZWUBuhRDYS5krHO1JS7l01X7eGnhTsb1bcOzV/asWigAds7Xou+mW1CVCQ/y52+XdiX5wHG+WH+g9jc4g65XAOKWUVFGLAwuJeXQCV5ckEJOfrF7RUJZ6Thc7yOnNd6eW5szjvPv77ZxUdfmvHBNL3x8asisT54FYa2hwwVOs68hXNW3DQPiInn+x1SOF7hBw6SwFjowwA39FjaJhYg0EZEmjjbG0LjIOFbAzR+u4a0lu7jopaVsSV6D8gv+PQrJHQiO0P95dzZOsThRVMqkmRtpHhbEK9f1xs+3ho+M/MOQvhh6X++S0h71QUT495geHC8o4aWFbuLs7jYGsrfBkXRXW3IGNYqFpUPefmAfsF9E9pmEPIM9yCss5U/T1lJUWs4HE5Po2z6SI3s2sUu1YvXe464270w6j4DDW3VRvEaEUorHv9rMweOFTJ7Ql4iQWiLmt3wOqhx6N6yvtrPp1jqciUPimLF6P1sz81xtDiReoR/drINetWIhIv8ALgcuUEpFK6WigAuB0ZbXDIZ6UVJWwb2frmfPkVO8d1N/Lu7Wgo9vG8DgsCPsUrFcP2UVf569kcMnilxtqiZhpH5MW+haO5zMp6v2MW/LIR4d2YX+7SNrPtmaW9EmSYc+exgPXdKZ6NAA/vntVtc7u5vG6vvoZltRNa0sbgbGKaV2WwcsP18HTHS0YQbvRCnFE19vYcWuozw3rpcuYw1IySmCCzK56LxhPHBRJ+ZtOcRFLy3l/eW7Xd9LOaaLzube2XjEYmtmHs98v4MLu8Rw53k2JNZlbYLs7W6VsV0Xmgb78/joRDbuP86XG9xgBdltLGQlu1XYdk1ioZRSf/hqp5QqBNwo7dHgSbz+UxpfbcjgoYs7c3X/SlnaR/R+sX/LRB4e0YWFDw1jUIdonp23g0tf/4UVu464yGJ0vkXCSNizDErdtImOHckvKmXSzA1EhQbw8nV9anZoW9k0C3wDoPs4xxvoIMb1bUP/9pE8Nz/F9aVAuo3Rj8mzXGtHJWoSi0xLxvYZiMhFQJbjTDJ4K1+uz+C1xWlc0z+WB4eflfl7VgHBuGahTL11AB9MTKKorJwb3l/NpJkbyMpz0Yd151FQWgB7f3XN9Z2EUoq/zdnKgWOFvHFDX6JCbajsU1ai+0h3uRRC3LR9qQ34+AhPj+3O8YISXl7kYmd3ZJz2XSx/0W3+5moSiweB90Rkmog8YDk+BqYAk5xjnsFbWJF+hMe/2szQTtH896oq4vRzUvQ308i4M4Yv7taCRQ+dz58vTmDR9sMMf3kZ7y7bRUmZkxe3cefqNqteHhU1a80Bvtt0kL9c0tn2vtVpC6HgKPTxLMd2VXRv3ZSbBrfn01X72HbQxc7usW9DdEf4/BY47vo8kGrFQim1DegBLAfiLMdyoIfltRoRkakiki0iWyuNvSgiKSKyWUTmiEhEpdeeEJF0EUkVkZGVxkdZxtJF5PF6/I4GF7PzcD53f7qeDjGhvH1jfwL8qvizy0mF6ATw/WOLlSB/X/58cWcW/+V8zunYjOfmpzD69eUcdGZfZf8giD9f51u4YSkGe7Aj6wT//m4b5yU0497zO9b+BiubZkFoc52T4gU8fEkXIkMCXJ/ZHRQO42dCeQl8dqPLt0BriobqBPRXSk1VSj1sOT4E+ldus1oD04BRZ40tQotNL2An8ITlWt2A8UB3y3veFhFfS4Xbt4DRQDdgguVcg4eQnV/EbR+tJcjfl49uG0jTYP+qT7S2Uq2BtlEhfHBLEh/dOoDM44X8d94OB1hcA51H6KKCOW4Sj29HThWXcf/MDTQN9ufV6230UwCcOgI7f4Re11Up9J5I0xB//jq6K+v3HePrjZmuNaZZAoybogMIvvuzS7+o1LQN9RpwoorxE5bXakQptRzIPWtsoVKqzPJ0FWD1cI4FZiulipVSe4B0YKDlSFdK7VZKlQCzLecaPIBTxWXcPm0dxwpK+OjWAbSJCK76xJICOLbP5jIfF3Ztzl3DOvL95izW7ztmR4trIWGEfvSybG6lFP/4Zit7j5zi9fF9adYk0PY3b/kSKsq8YguqMtf0i6Vvuwj+N28HeYUudnZ3GQ0X/h02z4bV77rMjJrEooVSasvZg5axODtc+0/AfMvPbYDKm3IZlrHqxg1uTnmF4sFZG9l2MI83JvSlR5saisodTQNUncp83D2sA83DAvnPD9tRzvq21TQWWvT0uhDaL9ZlMGdjJv83vDNDOkbX7c2bZkKr3tCiu2OMcxE+PsIzY3uQW1DCq4t2utocOO8R6Ho5LPg77FnuEhNqEouIGl6r5iuibYjI34EyYEZD5jlrzrtEZJ2IrMvJybHXtIZ6oJTi399t46eUbP49pnvt/Y7r0Uo1NNCPR0Z2YeP+43y/2YnBeZ1HwP6VUOjEFY0D2Xk4n3/N3co5HaOZVFVvipo4vE1vj3hYxrat9GjTlBsHtWP6yr1sP1jVJosT8fGBq96F6E7wxa0u6bFSk1isE5E/tE8VkTuA9fW9oIjcis4Mv1H9/pUwE6jUwJdYy1h1439AKTVFKZWklEqKiYmpr3kGO/Dhr3uYvnIfdw3rwM1nt9usipwU8PGrc1e1q/vFktgqnOfmp1BU6qQS4gkjdUmLXT8753oOpKCkjPtmbKBJoD+vje+Dr61+CivJM/W/W89rHGOgG/DIiC40DfbnX99udd4KtjoCwywO7zKYfaPevnUiNYnFn4HbRGSpiLxsOZYBtwP/V5+LicgodOOkMUqpyr/pXGC8iASKSDyQAKwB1gIJIhIvIgFoJ7h75cAbzmD+liydSNezJY+PsnGlkJMKUR3Br27den19hH9clkjm8UI++m1v3Y2tD7FJEBzlFVtR//p2G7tyTvL6+D40Dwuq25vLy2Dz5zr/JLSZYwx0AyJCAvjrqK6s23eMrze42NkN0KwTXP0BHNoC3z3oVId3TaGzh5VS5wD/BvZajn8rpYYopQ7VNrGIzAJWAl1EJENEbgfeBMKARSKSLCLvWq61Dfgc2I5u2Xq/Uqrc4gyfBCwAdgCf2xK2a3ANG/Yf48+fJdO3bQSv2Jr5CzZFQlXH0E7NuDixOW8tSefIyeJ6zVEnfHyh08WQvsijGyJ9tT6DL9dn8MCFnRjaqR4f9rt+hlPZHtG3oqFcl9SW3m0j+N/8FE4UudjZDXor9KK/60TIlW857bK2dMpbopR6w3LYvPZWSk1QSrVSSvkrpWKVUh8qpToppdoqpfpYjnsqnf+sUqqjUqqLUmp+pfF5SqnOlteerfuvaHAGRaXl/N/sjbQID+L9iUkE+dtYorqsGHJ3N6jh0ROXJlJUWu48R2TnkToJLXODc65nZ9Kz8/nHN1sZGB/Fg8MT6jdJ8gy9wrJGiHkx2tndnaOnit3D2Q3a4Z14BSz6J+xa4pRLmuZHBrvw7rJdHMgt5LlxPYmuS+jl0XRQFQ1qeNQxpgk3DW7PrDX72Xk4v97z2Ewnz22IlH2iiLs+WU9wgC+Tx/etuT9FdRQeg9R5OreijluHnkqv2AgmDGzH9JX7SDnkYmc36HplV74DzbrAl7c5peCgEQtDg9l39BRvL93FFb1bn64iazOnu+M1rJXqg8MTCA30c06iXnAktB2kk9E8iMMnihg/ZRWH8op47+b+tGxaRz+Fla1f66ziRrAFVZlHR3QhLMiPf33jJj27A8Ng/Az9ZWv2TQ53eBuxMDQIpRRPzd2Gv8XZXGeSZ0FgU52p2gCiQgN48KIElqbmsGynE0KnO4/QTsYTBx1/LTtw+EQRE6as4vCJIj7+00Db6z5VRfJMaN5N51c0IiJDA/jb6ETW7M3ltcVush0V3RGunqqbc82d5FCHtxELQ4NYvCObJak5PHRJZ1qE1/Gbatoi7Sg+/zHwq8PWVTVMPKc97aJCePaH7ZQ5ugeGBzVEOpSnVxTZ+cVMv72BQnEkDTLX6Yzts4tBNgKuTYrluqRYJv+czg/OzO+piYSLYfi/YOtXsGKywy5jxMJQbwpLynlq7jY6t2jCLefE1e3N5aWw4G86t2LgXXaxJ9DPlydGd2Xn4ZN8vs7BDWyaJ0JT92+IlJVXyPgpK8nJL+bjPw2kf/sGlhBPnqn9NT2vs4+BHoaI8MyVPejfPpKHv0h2jzasAOc+BN2uhMVPOSwHyIiFod68vTSdzOOFPD22B/51dZSumwpHdsKIZ+3qJB3VoyUD4iJ5ZVEqJ4vLan9DfRHRW1G7l0Kpm7R/PYuDxwsZP2UVR0+WMP32gbW3Rq2NinLYNFs7+MNqycr3YgL9fHnnpn5EhgRw1/R15OQ7IWS7NkRg7FsQkwg//g0q7L+yNmJhqBd7jpzivWW7uapvGwZ3qGM9oYJcWPJfXfK7y2i72iUi/OOybhw5WcI7S9PtOvcfSBgJpadgn3s0p6lMpkUoci1C0a9dA4UCdKfA/INeVzSwPjQP0yHiuQUl3Pvpeuf3V6mKwCYwYSbcPEeXB7EzRiwMdUYpxZNztxHo58MTl9Yjimnpc1B8Akb9zyH73r3bRnBln9a8/8seMo45MEIk/jzwC3a7raiMYwWMn7KSYwUlfHLHIPraQyhAByMENYXO9hV4T6VHm6a8eE1v1u075h7lQEA3Dwtv5ZCpjVgY6syCbYdZvlM7tetcJiInFdZ+AP1vdWil0kdHdUWAFxc4sPeEfzDED3OrhkgHcgsYP2UVeQWlzLhjEH3aRthn4qITsOM76HG1bgRlAOCK3q2ZdGEnZq89wPSV+1xtjkMxYmGoEwUlZTz93Ta6tgxj4pD2dZ9gwd8hoImuz+9A2kQEc+d5Hfg2+SAb9zuwQmznkToh6kia465hI1ahyC8qY8Ydg+kVG9HwScvL4OBG+OnfUFYIfW5s+Jxexl8u6czFiS14+vvt/JZ+xNXmOAwjFoY68ebP6RzMK+KZK3vUPfu3cqisE4rP3XNBR5o1CeQ/P+xw3BaBmzREsgrFyeIyZtwxiJ6xNfQPqYnSIti3Apa/CJ+Mg+fbw5QL9Gqw0yXQpr9d7fYGfHyEV6/vTceYUO6bsYF9R0+52iSHYMTCYDO7ck7y/i+7ubpfbN1j9R0QKlsbTQL9eHhEZ9bvO8b8rbXWvqwfEW2heXfY6Tqx2H9UC8WpEi0UNTaaOpuiE5C2GH56GqaOgufawkej4ef/QH4W9Loerv4QHtoON33ZKHMrbCEsyJ/3JyYhAnd8vI58dyg4aGe8o2munSgqLeeTlfu4rFcrWlfXArSRYs3UDvL35fHR9XBqW0Nlx89yaj2h65La8vGKvfxv/g6GJzYn0M/GAod1ofMIWPEGFOVpB7AT2Xf0FBOmrKKgtJwZdwyie2sbrr/3N0j5Hvb9prPQVYXOnWjdBwbdDe3OgXaDIaSBORmNjPbRobx9Qz9unrqGhz5LZsrNSbZXXvYAzMqiEkdPlfDiwlRecZfKkm7EvC2H+CXtCI+M6EJMWB2zra2hsh0usHuobG34+gh/vyyRA7mFfLxir2MukjBS96F2ckOkzRnHuf69VRSWljPzjsG1C8XxA/DZzTDtUi3egeEw7FGY+C08cQDu/BlG/Ae6XmqEop6c06kZT17RjcU7snl5kQODK1yAWVlUok1EMLeeE8f7v+zmjvPi6doy3NUmuQWnist45vvtdGsVzo2D2tV9gmXP61DZkf91yTbGeQkxXNglhjd+SufqfrF1q4prC7EDdHHBnQuh+1X2nbsaPl93gH98s5WYJoHMumtwzX+rZcWw8k1Y/pKO2rroHzDkARPV5CBuHtyeHVkneGvJLjq3CGNsnzauNskumJXFWdx3QUfCAv14bn6Kq01xGyb/nMahE/V0auekwpr3HR4qWxt/uzSRgtJyHpi10f6Z3b5+lRoiOTY5q6Ssgr/P2cJjX25mYFwU3z1wbs1Ckf4TvHOO9kl0vAgmrdGrCSMUDkNE+PeYHgyIi+SxLzezJcNNSoI0ECMWZxEREsD9F3ZiaWoOK3bZGAZ3fD9kpzj8g8IVpB3O58Nf9nBdUmz9ykU4KVS2NhJahPHC1b1YvSeXG99fRe6pEjtfYCScytFhpg5ClxhfyYzV+7nn/I5Mu20AUaHV+H+OH4DPboJPx2mfxI1f6XLWEfVYGRrqTICfD+/c1J9mTQK565N1ZOe7Z0mYumDEogpuOSeO1k2DeG5+Su116wuP6dDCtwfpMMOPx+hvcanz4aQTSmU7EKUU//p2GyEBvvzV1n7alXFyqGxtXN0/lndv6s+OQ/lc++4KDh4vtN/knYaD+DgshHbt3lwum/wrKYfyefvGfjw+umvVq7yyYr3d9OYAHeV00T/hvlW6MqnBqTRrEsiUif05XlDKPZ+s5/AJzxYMcYsUdTuTlJSk1q1b16A5vlyfwSNfbOKNCX25onfr6k+c/ziseQ8ueQaO7YGMtXB4m3Z4gv4m1yZJ72vHJkHLXh6zBTB300EenLWRZ67swc2D65iAV14K7wyFilK4b7VbdVRbtfsod368jrAgP6bfPohOzZvYZ+IPR+rEtbuX22c+tGBPX7mPZ77fTtuoEN67uT+dW4RVfXL6Ypj3GOTu0i03R/7XrCTcgHlbsrh/5gaUgn7tIhjVoyUju7ekfXSoq037AyKyXimVVOVrRiyqprxCcdnkXygoKWfxX84nwK+Kb3HZKXo/uN9EuOK138dLCyFrkxaOjHWQuR7yDujXfPyhZQ8tIImX6wghN+RkcRkXvbSUFuFBfHP/UHzrGgK4+j2Y/5gOle16qWOMbABbM/O49aM1VCiYdtsA+2Q7//KKznR+OBXCWjZ4uqLScv729Ra+3pjJxYnNefm6PjQN9v/jiccPwIIndDmOqA4w+kWzknAzduWcZP6WLH7cdoitmbota9eWYYzq0ZJRPVrSpUUY4gY5LEYs6smS1Gxu+2gtT13RjVuHxp/5olJ6PzhzPTywofZtlvxDFuFYZ3ncAKUFcOXbblnF8z/fb+fD3/Yw576hda8vVJALk/vquP2bv3HbRK49R05x0werOV5QwvsTk+reEvZsDm/TXx7GvKG/QDSAA7kF3PPperZnneDPwzvzwEWd/hizn7sbNn0Gv72unw97BM55wC6NpAyO40BuAQu3H2bB1kOs3ZeLUhAXHcJIy4qjT2yEy/IzjFjUE6UUN36wmpRD+Sx79ALCgip9q0udD7PGw6jnYPC9dZ+8tFC/f/cy3Xi9j/v0M16RfoSbPlzN9QPa8r9xveo+wfy/wpopcM+vLo2AsoVDeUVMnLqavUcKmDyhD6N6NKBip1Lwag8tkuNn1HuaX9JyeGDWRsorFK+P78NFXVv8Pn9WMqT8oI/s7XrcbDl5LDn5xSzafpgftx1i5a4jlJYrWoQHMrJ7S0Z1b8mA+Ki694ppAEYsGsCWjDyuePNXHrioEw+P6KIHy4rh7cHg4wf3rgDfKrYGbKGkQAvGnuVw1XvQ+3q72NwQsk8UcenkX2ka7MfcSecSGljHVJycVHh7CPS/BS5/1TFG2pnjBSXcNm0tmw4c53/jenL9gAZ86H7/EGz+HB7bXedv+Eop3l22mxcXpNCpeRPeuzmJ+MgA2PsrpM7TAnEiUzvS250DXS/TW3yRcfW31+A25BWWsiQlmx+3HmLpzmyKSitoEujHkI7RDEtoxrDOMQ73c9QkFg5LyhORqcDlQLZSqodl7FrgKSARGKiUWlfp/CeA24Fy4EGl1ALL+CjgdcAX+EAp9ZyjbK6KnrFNuaJ3a97/ZTc3DW6v+0yvfldvAdz0Vf2FAiAgBCbMhpnXwTf36O2aXq5rV1lWXsGDszdysliXt66zUIDbhMrWhYiQAGbcMYh7P93AX7/awrGCUu45v2P9JksYqbOj962Ajhfa/LbDJ4r493fbmLflEON6NOW/PbMJWvZnHV1VlKf7ZnQarhPqEkZCaB0bThncnqbB/lzZtw1X9m1DYUk5y9NyWLYzh+U7c1i0/TAA7aJCOM8iHEM6RhMe1IDPnzriyAzuacCbwPRKY1uBccB7lU8UkW7AeKA70BpYLCKdLS+/BVwCZABrRWSuUmq7A+3+A4+O6MKPW7N4bfFO/ndJC1j2om4A08kOTsSAELjhM5h5Pcy5W39r7HlNw+etB6//lMaq3bm8dG1vurSsJuKmJqyhsiOedYtQ2boQEuDH+xOTePiLTTw3P4Vjp0p4fHTXujsd44eBX5AuLFiVWJSXQclJKDkFJSfZlXmYBRvS2bQ7k+bksrzNTtruWYukF0NwFHS9XK8gOlyo/1YMjYLgAF9Gdtc+DKUUe48W8EtaDst3HuGbjZnMWL0fXx+hb9sIzkuIYVjnZvSKjah7IEodcJhYKKWWi0jcWWM7gKr+A44FZiulioE9IpIODLS8lq6U2m1532zLuU4Vi3bRIdw4qD3TV+7liZI3CS8rgpHP2u8CAaFaMGZcB1/fqVcYPa623/w2sDQ1mzd+Tufa/rFc0z+27hMopSOBnFhV1t4E+Pnw+vV9iAzx573luzlWUMJ/r+pZt6z1gBAtGMkzdfBDycnfxaH4JJSf2a+5I3Af/P4/sbw9DLhDC0TbQTo73NCoERHim4US3yyUiUPiKC2vYMO+Y/ySdoRf0nJ47aedvLp4J02D/RnaKZqLurao3//hWnCXv8Q2wKpKzzMsYwAHzhofVNUEInIXcBdAu3b2d/Q9cFEnUtYvJTzlMzjnQYiu5zZFdQSEwo2fw4xr4as7AYEe4+x7jWrIyivkoc+S6dIijKfH9qjfJGkLdQXTK99xq5yKuuLjI/x7THciQwJ4/ac0jheUMnlCX4L861CtdtDdeuvIPxiaNNfbcgGhlPqFsP1oBb/tL2T/SR8CgsMYktie83rE0aRJUwiOgMh4t40eM7gH/r4+DOoQzaAO0Twysgu5p0r4Lf3I6ZXHsVOlXi0WDUYpNQWYAtrBbe/5o0MDeLXpbHLymnIg7g762fsCYFlhfA4zroGv7tAfGg4uTFdaXsEDMzdSUlbB2zf1IzigHiW8ldJZw03bQc9r7W+kkxERHrqkM5Eh/jz13XbOe2EJPVqHk9jq9yO+WWj1S/5OF5+xRZmdX8QnK/fx6ap9HCsopWebptwxOp5Le7ZyaqSLwTuJCg3git6tuaJ3a5RSnCiyc+0zC+4iFplA20rPYy1j1DDuXLZ8QasTm3na736SF2fwVUI7xyTRBDaBG7+AT6+BL2/XPoxuY+1/HQsvLUhl3b5jvD6+Dx1j6pnJvPdXyFgDl77UMIe/m3Hr0HjaRoXw3aaD7MjKZ3naEcot5V+C/H3o0jKcbq3CTgtI15ZhZ4RXpxw6wYe/7OHb5IOUVlRwcWIL7jg3noHxUW6RgGXwPkSk6sRNO+AuYjEXmCkir6Ad3AnAGkCABBGJR4vEeMD5GWzFJ2HRv6BVHxL63MXUOdtYsO0wo3o0PEu3SgLDdFeyT6+GL/8E13wE3cbY/TKLtx/mveW7uXFQu4aVUf7lJQhtDn1vtp9xbsLwxBYMT9R5DsVl5aQdPsmOrBPsyMpnR9YJ5m05xKw1v++Uto0KJrFlOKdKyvgt/SjB/r6MH9iW24bGE9/M/co7GAy24sjQ2VnABUAzEckAngRygTeAGOAHEUlWSo1USm0Tkc/Rjusy4H6lVLllnknAAnTo7FSl1DZH2Vwtv72mW0xe+zHXtmnHB7/u5YUfUxie2Nxx2wiBYXCjVTBug2s/1uVB7MSB3AIe/mIT3VuH88/Lu9V/ooz1sHspXPK0x9S8qi+Bfr70aNP0jLalSimy8oosAvK7iJSUV/DoyC7cOKgdESGe68MxGKyYpLzaOLYX3hyot4Kufh+AhdsOcdcn6/nPlT24qa4F9upK0QldVuTgRrhuuo6SaSAlZRVc+95Kdmef5PsHz21Yos+sCTqn4KGtWuAMBoPHUlNSnvGu1cbCf4KPL1z81OmhS7q1IKl9JK8tTuOUvRvpnE1QuE7+a9UHPr8FUuY1eMr/zd/BpgPHeeGaXg0TisPbdGbx4HuNUBgMXo4Ri5rYsxx2zIVz/wJNf9/TFxGeuLQrR04W88EvexxvR1BTuPlraNULPp+oyz7Uk/lbsvjot73cNjSO0T0bUAcJdJXVgCYem1dhMBhsx4hFdZSX6V4VEe3gnEl/eLl/+yhGdm/BlOW7OHKyuIoJ7ExQU7ipkmBsn1vnKfYdPcVjX26md9sInhid2DB7ju6CbV9D0p8gJKphcxkMBrfHiEV1bJgG2dtgxH90clUVPDaqK0VlFUz+Kc05NgVHwM1zoHU/+OJW2DbH5rcWlZZz34wN+PgIb93Qt+r+HHXh11d1b44hfxRSg8HgfRixqIqCXPj5WYg7DxKrD1ntGNOE8QPaMnP1fvYcOeUc26xbUm0H6jyMLV/a9LZnvt/OtoMneOW63sRGNrDGUF4GbJqtezaEtWjYXAaDwSMwYlEVy56HouO6V0UtyVP/d3ECAX4+PPx5MnmFpc6xzxpW226IriW1aXaNp3+brAuP3X1+h9M5Aw1ixRuAgqEPNnwug8HgERixOJvsHbDmfeh/m25/WgvNw4J46drebMnMY8KUVc7xX4Al0/tziDsX5twDG6tutrMkNZsnvt5CUvtIHrH242gIJ3Ng/cfQ63rTbMdgaEQYsaiMUvDjE/qDuA79GC7t2Yr3Jyax+8hJrnt3JZnHCx1oZCWstaQ6Xgjf3q8/xC1UVCje+CmNP01bS7uoEN66sZ99EghXvQVlRXDuQw2fy2AweAxGLCpzdBfsXwkX/K3OzWUu6NKcT24fRE5+Mde+s4LdOScdZORZ+AfD+Fm6Mc53D8LaDzlRVMrdn67n5UU7GdO7NXPuG6qbNjWUwuOw5gOdoNgsoeHzGQwGj8GIRWWadYJJa2HA7fV6+4C4KGbdNZjisgque28l2w7m2dnAavAPgvEzofMo+OEvTHvlb/ycks2TV3Tjtev71K+SbFWseR9K8uG8h+0zn8Fg8BiMWJxNRLsGVU7t0aYpn98zhABfH8ZPWcW6vbl2NK4G/AKZ1+0FflJJPFgyhZ/P2cptQ+PtV920+CSselu39GzVyz5zGgwGj8GIhQPoGNOEL+49h5gmgdz04WqW7cxx6PXKyiv43/wd3Dd7K+80/xdFnS6n/bpn4bfJ9rvI+mlQmGtWFQZDI8WIhYNoExHM5/cMoUOzJtzx8VrmbclyyHVyT5Vwy0dreG+ZLjU+4+5zCZowTTdNWvRP+OXlhl+krFiHy8adB+2qbFRoMBi8HCMWDqRZk0Bm3TWY3rERTJq5gc/W7rfr/Fsy8rjijV9Zu/cYL1zdi2ev6kmgn6/eRhv3ge5a99PTsOyFhl0oeQacPGRWFQZDI8aIhYNpGuzP9NsHcm5CDH/9agsf/LLbLvN+se4AV7+7AoAv7xnCdQPannmCrx9c9R70ngBLnoWPr9B9J+pakr68DH59TZcY6XCBPUw3GAweiBELJxAS4McHE5O4rGcr/vPDDl5emEp9+4iUlFXwj2+28OiXmxkQF8ncSUPpFRtR9ck+vjD2LRj5P8jZCdPHwgcXQ+p820Vj65dwfB8Me6TWbHaDweC9uEtbVa8nwM+HyRP60iTQjzd+TudEYSlPXtEdH58zP4CLy8rJLyqzHKWnH09Yxr7ffJCN+49z9/kdeHREF/xqS7Tz8YUh9+nqsMkzdNe/WeOhRQ847y/Q7Up9TlVUVOgy5M27QefRdrkPBoPBMzFi4UR8fYTnru5JeLAf7/+yh7V7j+HvK+QXlXHCIgglZRU1zhEW5MdbN/Tjsl517EXhH6TzR/pN1MUHf31F9/eOelZnY/e6HvzOav+Z8j0cSYWrPwQfswg1GBozpq2qC1BK8eGve5i3JYsmQf6EBfkRHuRHWJA/YYF+hAfrsbAg66Mf4UG/j/n62GE7qKICUr6D5S/Boc0QHgtD/w/63ayzwpWCKefrtq6T1mkfiMFg8GpqaqtqxKKxoxSkL9aicWAVhMbAkPshMk73zLhiMvS/xdVWGgwGJ1CTWJivi40dEUi4RB97f4NfXoLFT+nXwtvoaCqDwdDoMWJh+J24ofrIXA+rp0DiFX/0YxgMhkaJEQvDH2nTH8a952orDAaDG+GwEBcRmSoi2SKytdJYlIgsEpE0y2OkZVxEZLKIpIvIZhHpV+k9t1jOTxMRs3luMBgMLsCR8ZDTgFFnjT0O/KSUSgB+sjwHGA0kWI67gHdAiwvwJDAIGAg8aRUYg8FgMDgPh4mFUmo5cHZ97rGAtZ3bx8CVlcanK80qIEJEWgEjgUVKqVyl1DFgEX8UIIPBYDA4GGdnWrVQSlnLrx4CWlh+bgMcqHRehmWsuvE/ICJ3icg6EVmXk+PYkuAGg8HQ2HBZWq7SCR52S/JQSk1RSiUppZJiYmLsNa3BYDAYcL5YHLZsL2F5zLaMZwKVy6bGWsaqGzcYDAaDE3G2WMwFrBFNtwDfVhqfaImKGgzkWbarFgAjRCTS4tgeYRkzGAwGgxNxWJ6FiMwCLgCaiUgGOqrpOeBzEbkd2AdcZzl9HnApkA4UALcBKKVyReQZYK3lvKeVUk5qam0wGAwGK15ZG0pEctBiVF+aAUfsZI4nY+6DxtwHjbkPGm++D+2VUlU6fb1SLBqKiKyrrphWY8LcB425DxpzHzSN9T6YJgUGg8FgqBUjFgaDwWCoFSMWVTPF1Qa4CeY+aMx90Jj7oGmU98H4LAwGg8FQK2ZlYTAYDIZaMWJhMBgMhloxYmEwGAyGWmmUYiEicZWbMtVybpUNm7yBOt6Hp0QkU0SSLceljrbPkdTxd79WRLaJSIWIJJ312hOWpl2pIjLSMdY6DnvcB8schZX+Nt51nMWOoY734UURSbE0apsjIhGVXvPov4eaaJRiUUeqa9jUGHlVKdXHcsxztTFOZCswDlheeVBEugHjge7oPitvi4iv881zGlXeBwu7Kv1t3ONku5zNIqCHUqoXsBN4Arz/76Exi4WviLxv+aa0UESCqzmvuoZN3oKt98Ebsel3V0rtUEqlVvHSWGC2UqpYKbUHXdtsoCMNdhANvQ/egq33YaFSqszydBW6GjZ4z99DlTRmsUgA3lJKdQeOA1dXc151DZu8BVvvA8Aky9J7qpdsx9Xld68Km5tzuTkNvQ8A8SKyUUSWich5drXOedTnPvwJmG/52Vv+HqqkMYvFHqVUsuXn9UBcbW+wd8MmN8HW+/AO0BHoA2QBLzvaMCdQ578BL6Wh9yELaKeU6gv8BZgpIuH2M89p1Ok+iMjfgTJghmPNcg8as1gUV/q5nOrLtVfXsMlbsOk+KKUOK6XKlVIVwPt4x/La1r+B6vCW5lwNug+WbZejlp/XA7uAzvYzz2nYfB9E5FbgcuBG9Xtms7f8PVRJYxYLW6muYVOjwiqYFq5COzsbO3OB8SISKCLx6G2MNS62yemISIzVkSsiHdD3YbdrrXIcIjIKeAwYo5QqqPSSV/89OKz5kRdRXcOmxsYLItIHvQ23F7jbpdY4ERG5CngDiAF+EJFkpdRIpdQ2Efkc2I7ejrhfKVXuSlsdSXX3ARgGPC0ipUAFcI+XNyl7EwgEFokIwCql1D3e/vdgakMZDAaDoVbMNpTBYDAYasVsQ1kQkbeAoWcNv66U+sgV9riKxnwfGvPvXhlzHzTmPpyJ2YYyGAwGQ62YbSiDwWAw1IoRC4PBYDDUihELg8FgMNSKEQuDwWAw1Mr/AwE1jabc+aOqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ycBNvjFncLSKrXN1UUa6ydsDOCsdkuMqqKlf+ZN8WyF5bdRdUuehOOmbRGGSk2a/enAlVLrarBgs38XSweBXoDPQFdgP/dNeJReRWEUkTkTRNFuhj1s+wX2sKFlFJcOSAvSn/lbEUmp8CrRqQbNJdYpIhdycUFXi7Jn7Po8HCGJNljCk1xpQBr/NrV1Mm0L7CoQmusqrKKzv3JGPMAGPMgLi4OPdXXtXf+unQth9Etq/+uPIZUTpu4d8yltouKBFv18Q1I8rA/i3eronf82iwEJGK6/7HAuUzpaYDV4lIqIgkAcnAEmApkCwiSSISgh0En+7JOqsGysuAzGU1typA11o0Bof32Z+fL3RBgc6IciPHBrhF5H3gLCBWRDKAR4GzRKQvYIDtwG0Axpi1IvIRduC6BLjLGFPqOs/dwBwgEJhsjNH9N/3Jhpn2a/neFdWJSrRfddzCf2WWj1d4eXC7XExnQDRHlBs4ORvq6kqK36zm+CeAJyopnwXMcmPVlCetnwFx3X9dIFWdkAjb163dUP4rI82ul2nbz9s1sYKbQWQHbVm4ga7gVs45nAM7fqxdq6JcdCcNFv4sYym07nVyokhv0hlRbqHBQjlnw0wwZbUbryinay38V1mZHZ/ylfGKcrHJdmFeWZm3a+LXNFgo56yfAZEd4ZQ65AeKToJDe6DosHP1Us7I2WT3VvfFYFFcAAcrnUipakmDhXLG0TzY+p1tVdRlCmWUa0aUboTkf45lmvW1YKEzotxBg4Vyxqa5UFZct/EKqDB9Vsct/E7GUgiLhOjO3q7J8cqDheaIahANFsoZ66fbmU11/ZR5bGGejlv4ncxl0K6/zcnkSyLiIKyVtiwayMd+qqpRKCqAzV9Dj0vq/o+jWZT9dKprLfxLYT5kr/O9Liiw3aA6I6rBNFgo99vyjR1QrMssqIqiO2nLwt/s+tnOfPPFYAE2R1ROurdr4dc0WCj3Wz/DthA6Dqvf66OTdMzC35QPbrdL9W49qhKbDPm74ehBb9fEb2mwUO5VUgQbZ0O3iyAwuH7niO4EeTvtuZR/yEizn97Do71dk8odG+TW1kV9abBQ7rV9IRTm1b8LCuz0WVNmA4byfca4Ms36aBcUVJg+qzOi6kuDhXKvddMhpDl0Orv+59BU5f4l9xc4vBcS+nu7JlWLToKAIB3kboAag4WIXCYi6SKSJyIHRSRfRLTjT52srNSm+EgeCcFh9T+Ppir3L766GK+iwGDbYtVgUW+1yTr7NDDKGLPe6cooP/fLYijIaVgXFEDz1hAcrtNn/UVGGgQ1g/he3q5J9WK76oyoBqhNN1SWBgpVK+tnQGAoJJ/XsPOI2E+B2rLwDxlL7SyoQMd2PHCP2C52x7zSEm/XxC/V5qebJiIfAp8DheWFxphPnaqU8lMZS6D9IAht0fBzRSfpp0B/UFIIe1bBkDu8XZOaxXaF0iLI3eHaFEnVRW1aFi2BAmAkMMp1u8TJSik/VFYK2evrlmG2OtFJNpmgppX2bbtX2X/AvjxeUU5zRDVIjS0LY8yNnqiI8nMHtttV263d1G8d3QlKCyF/F7RKcM85lfsdW4znI9uoViemi/2aswm6nu/duvih2syGShCRz0Qk23X7RET0r1cdL2uN/equYBGlM6L8QmYatEyAlm28XZOahUfbpII6I6peatMN9RYwHWjrus1wlSn1q6y1du/luO7uOZ+utfAPGUshwQ9aFeV0RlS91SZYxBlj3jLGlLhuU4C4ml4kIpNdLZE1lTz3exExIhLreiwi8pKIbBaRVSKSWuHYCa51HukiMqEO35vypKy1tpkf3Mw952uVAAHB2rLwZflZdkGeP4xXlIvpoi2LeqpNsNgnIteJSKDrdh2wrxavmwJccGKhiLTHDpb/UqH4QiDZdbsVeNV1bDTwKDAYGAQ8KiJRtbi28rSsNe7rggIICISojrrWwpdlptmv/hQsYrtCwT4o2O/tmvid2gSLm4ArgD3AbmAcUOOgtzFmIVDZT+R54EHAVCgbA0w11mIgUkTaAOcD84wx+40xB4B5VBKAlJcV5tsBbncGC9C1Fr4uY6lt/bXp4+2a1N6xHFHaFVVXtZkNtQOo496YlRORMUCmMWalHL8vczugYta4DFdZVeXKl2S71my2PtW9541OsqvCjanbPt7KMzLS7FRpd3U9ekJssv2aswk6DPZuXfxMlcFCRB40xjwtIv/i+FYAAMaYe+pyIREJB/6A7YJyOxG5FduFRYcOHZy4hKpK+Uyo+J7uPW90JyjKh8M50LzGYTLlSWWlkLkc+l3r7ZrUTWQHm2VAxy3qrLqWRXmKjzQ3XaszkASUtyoSgOUiMgjIBNpXODbBVZYJnHVC+XeVndwYMwmYBDBgwICTgptyUNZaCGlh/xDdqXz67IFtGix8TfZ6KD7sX+MVYMfCYrpoN1Q9VBksjDEzXHcLjDEfV3xORMbX9ULGmNVAfIVzbAcGGGNyRGQ6cLeIfIAdzM4zxuwWkTnA3ysMao8EHqnrtZXDstba8Qp3dxUdmz671aYRUb7jWKZZP5o2Wy62C+w5aZKmqkFtBrgr++dc4z9sEXkf+AnoJiIZInJzNYfPArYCm4HXgTsBjDH7gb8BS123x1xlylcY82uwcLeojoDoWgtflJEG4TG/tv78SWxXOyFDd2Ksk+rGLC4ELgLaichLFZ5qCdSYttEYc3UNzydWuG+Au6o4bjIwuabrKS/J2wmFB50JFkGhdr2FzojyPeU74/njxIPYrmBKbfdmXDdv18ZvVNey2IUdrzgKLKtwm46d0qqUbVWA+2dClYtK1LUWvuZILuRs9M8uKDh+RpSqterGLFZiB6PfM8YUe7BOyp8cmwnVw5nzR3eCDV86c25VP5nL7Fd/G9wuF6PBoj5qs59Fooj8A+gJHNsr0xjTybFaKf+RtRYiO0JYS2fOH51kV9wezYOwVs5cQ9VN5jJAoG1qjYf6pNDm0LKdzoiqo9omEnwVO05xNjAV+K+TlVJ+JGutc11QoAkFfVHGUpsw0qkPCJ4Qm6wtizqqTbBoZoz5BhBjzA5jzF+Ai52tlvILxUftRjJODG6Xq7jWQnmfMf6XabYyMcm2ZWF0SVZt1aYbqlBEAoB0Ebkbu1CuubPVUn5h7wYwZc4Gi2jd18Kn7N8KRw7473hFudiudhbfoWxo0drbtfELtWlZ/A4IB+4B+gPXAZoqXDk/Ewrsft4RcdoN5SuOLcbz92Chg9x1VW2wEJFA4EpjzCFjTIYx5kZjzOWuzLCqqctaC0HNfv3075ToTnYRlfK+jKU2tYu/r084ln1Wg0VtVRssjDGlwOkeqovyN1lr7JTZgEBnr6Opyn1HRhq06+f8z9xpLdtCcITOiKqD2nRD/Swi00XkehG5rPzmeM2UbzPG/RseVSW6ExzMhOIjzl9LVa2owP7M/b0LCuzKc50RVSe1GeAOw+6Md06FMgN86kiNlH84lG3XPzg5XlGuvJvrwA6Id9Me36rudq+EspLGESzAptTfNBtKiyEw2Nu18Xm1CRZvGGN+rFggIsMcqo/yF+Urtz3VsgA7fVaDhfeUD2638/Nps+V6jIKV78GW+dBVMxjVpDbdUP+qZZlqSo7NhPJAsIjS6bM+IWOpzdXVWPYW6XIuNIuCVR95uyZ+obqss0OB04A4EbmvwlMtAT8f3VINlrUWWrSB8GjnrxUeDaEtdfqst2WkQWIj6lQICoGel8KqD6HwkE0DoqpUXcsiBLv4LghoUeF2EBjnfNWUT3NqD4vKiNhxC21ZeE9eJuTvajzjFeX6XAHFBbBhprdr4vOqyzq7AFggIlOMMTs8WCfl60qL7ertLufUfKy7RCXBnlWeu546XqZrd+XGMl5Rrv0QaNUBVn8EKVd6uzY+rTZjFqEiMklE5orI/PKb4zVTvisnHcqKPTMTqlx0J8j9BUpr3HdLOWHjbAgMhVM8+DP3hIAA6D3ODnIfyvZ2bXxabYLFx8DPwJ+AByrcVFPlycHtctFJdtpm3k7PXVNZ66bbWUODJtrdCxubPlfYHGdrdDVAdWozdbbEGPOq4zVR/iNrDQQE/7qJjCccS1W+1fn0IupX+7fBF3fbvStGPOrt2jgjvge07m27oobc7u3a+KzatCxmiMidItJGRKLLb47XTPmurLU2N1BQiOeuqanKPa+kCKbdZO+Pf8uzP29P6zPebuq0b4u3a+KzahMsJmC7nRbx6z7caTW9SEQmi0i2iKypUPY3EVklIitcYyBtXeUiIi+JyGbX86kVXjNBRNJdN8126ws8OROqXIs2EBSm02c96etHYddyuPRlu76iMTt1HCCw+mNv18Rn1RgsjDFJldxqs6XqFOCCE8qeMcb0Mcb0Bb4E/p+r/EIg2XW7FbszH64WzKPAYGAQ8KiIRNXi2sopBfvtFEpPB4uAAPsPS4OFZ6z/Eha/AoNvtyudG7tW7SDxdLtATzdEqlSNwUJEwkXkTyIyyfU4WUQuqel1xpiFwP4Tyg5WeBiBzTEFMAaYaqzFQKSItAHOB+YZY/YbYw4A8zg5AClPyl5nv3o6WIAdt9C1Fs47sAO+uBPa9IXzHvN2bTynzxWwfwtkLvd2TXxSbffgLsKu5ga7U97j9b2giDwhIjuBa/m1ZdEOqDjNJcNVVlW58hZPbHhUlagku69FWZnnr91UlI9TGAPjpzTO2U9V6TEaAkPsQLc6SW2CRWdjzNNAMYAxpgCQ+l7QGPNHY0x74F3g7vqe50QicquIpIlI2t69e911WnWirDUQHgPNvbAVZXQSlByBQ3s8f+2m4pu/2gV4o//V9GadNYu0CQXXfKLreSpRm2BRJCLNcHUZiUhnoNAN134XuNx1PxNoX+G5BFdZVeUnMcZMMsYMMMYMiItrJInOfFH54LbU+/NC/R3bj1vHLRyx8Sv46d8wcCL0utTbtfGO3lfA4b2w7Ttv18Tn1CZYPArMBtqLyLvAN8CD9bmYiFScmD8G2OC6Px34jWtW1BAgzxizG5gDjBSRKNfA9khXmfKGslLIXu+dLig4fq2Fcq/cnfDZ7XBKHxhZ715m/5c8EsJawSqdFXWiGhflGWPmichyYAi2++l3xpicml4nIu8DZwGxIpKBDToXiUg3oAzYAZSvgJkFXARsBgqAG13X3i8ifwNcifR5zBhz3KC58qAD223SNW8MbgO0ag8SqGst3K20GKbdaD8MjJ8CwWHerpH3BIdBzzGw+hMoeg5CIrxdI59RY7AQkbHAfGPMTNfjSBG51BjzeXWvM8ZcXUnxm1Uca4C7qnhuMjC5pnoqD/DkhkeVCQyGyA7asnC3bx6ze1WMewtiOnu7Nt7X+wpYPtV2y/XWBNvlatUNZYzJK39gjMnFthJUU5O1FiQA4ry4W110ko5ZuNOmObDoJRhwE5x6mbdr4xs6DoOW7XRTpBPUJlhUdkxtckqpxiZrLUR3huBm3qtDdCcbLHThVMPlZcBnt9m8SOf/w9u18R0BAXDq5bDlGzi8z9u18Rm1CRZpIvKciHR23Z7DpvxQTU3WGu91QZWLSoLCPDhywLv18HelxXY9RWkxXPF20x6nqEyfK2yW47WaibZcbYLFb7GL8j503QqpYnxBNWKF+XaA21szocpF637cbjH/cdj5Pxj1oo5TVKb1qRDfU7uiKqjNbKjDwMMeqIvyZdnr7VdvtyyOTZ/dBgmNbNc2T0mfBz++AP1v0AHcqohA7/F2keL+bU1vgWIlapMbqqvulKe8PhOqXHn2U50+Wz9lpfDlffZT8wVPers2vq08kK6e5t16+IjaDFR/DLwGvAGUOlsd5bOy1kJICzt11ZuCm0GLttoNVV/p8yDvF7hiqncnKviDyA7Q4TSbK+qM+72TtcCH6E55qna8mebjRDp9tv7SJtu8Xt0u8nZN/EOf8fDl/8HuldC2r7dr41W6U56qmTHe2fCoKtFJ2rKoj9xfIH0upP7GLnBUNet5qd1CWAe6ndspTzUieTuh8KDvBIuoJDicDYWHvF0T/7LsbdsyTNUNJ2stPNrmi1rziR3vacKc3ClPNRbe3MOiMuUzonSQu/ZKi20Ki+TzIbJ9zcerX/UZb9Pib1vo7Zp4VW1mQwWLyD0iMs11u1tEtA3blJTPhIrv4d16lCuvR6auDa21DV/a1tiAm7xdE//T9QI7uaOJ789dmwHuV4Fg4BXX4+tdZbc4VSnlY7LWQWRHCGvp7ZpYcd3tFNr1X9q1AqpmaZOhVQfoMsIjlysqKWPDnoNs3XuY8JBAWjULplV4MJHNQmjVLJiw4ADEFyZL1EZwM+g5GtZNh4v/2WRnkdUmWAw0xqRUeDxfRFY6VSHlg7LW+k4XFNh+9x6jYPFrcDTP7j+gqpaTbrtQzvkzBAS6/fTGGHblHeXnXw6w4pdcft6Zy5rMPApLqt7+NiQwgJbNgmnVLMgGkmbBRIbbQBIVHsKYvm1JjPWh9OC9x8OKd20m2iaacLE2waJURDobY7YAiEgndL1F01F8FPal2xz/vqTHaFj0L5s1tc8V3q6Nb1s2BQKC7CwoNzhcWMKqjDx+3vlrcNibbzfPDA0KoHe7Vlw/pCN9O0TSrXULjhaXkXek+Lhb7pEiDlZ4vPdQIZv3HiKvoJj8whL+/W06E4Ym8ttzkmkV7gO93klnQPNTbFeUBosqPQB8KyJbsZsfdcS1OZFqAvZuAFPmOzOhyrUbYP9410/XYFGd4iPw839tS6x5fL1OcaSolG83ZvN9+l5+/iWXTVn5lLmS/ibFRjC8Syx9O0TSr30U3du0IDiwNpMsq5Z98Cj/nLuJN3/cxifLM7j33K5cM7hDg8/bIAGBdkX3//4DBfvtLKkmpja5ob5xbYfazVW00Rjjjj24lT/wtZlQ5QICoMcl8PO7UFQAIeHerpFvWvs5HM2t88B2eYCYuXo389dnc6S4lJZhQfTtEMX5vU6hb4dI+iZEEhUR4vYqx7cM46lxfZhwWiKPz1zHo9PX8vZP2/njRT04p3u898Y6eo+3e5Sv+7xJThSozU55dwHvGmNWuR5HicjNxphXanipagyy1kJQM99MpNZjNCx9w+470GOUt2vjm9ImQ0wXSBxe46GVBYjY5iFc3r8dF/Vuw+CkGAIDPPePumfblrx7y2C+WZ/N32et5+a30xjWJYY/XtSTnm29MNmiTQrEdrX7c2uwqNREY8zL5Q+MMQdEZCK/zo5SjVnWGojv7sjAaIN1HAbNomD9DA0WldmzGjKWwPl/rzJNy5GiUr7bmM2XPhIgTiQinNuzNWd2i+PdxTt44Zt0Lv7X91w5oD33jexKfAsP7sMhAn2uhPl/gw0zofvFnru2D6hNsAgUEXHtk42IBALub3sq32OMDRbdLvR2TSoXGATdLrbBoqQIgvTX8jhpb0FQGKRcfVxxeYCYuXo38zdkU1BUSkxECJeltuPi3m0YlBRNkDfHByoRHBjADcOSGNsvgZfmpzP1p+3MWLmLO87qzC3DOxEW7KEPM0PuhI2zYNrNcMOXTSpNfm2CxWzgQxH5j+vxba4y1dgdyoaCfb43XlFRj1Gw4r92amjyud6uje8ozIdVH0KvyygJjWTVLwdYtDmHHzbnsHxHLkWlZcREhDC2n+8GiMq0Cg/mz5f05LohHXnyq/U8O3cT7/3vFx66sDuj+rQlwOlWUEg4XP0hvDEC3rsSbpn3a0aBRq42weIh4FbgDtfjedh05dUSkcnAJUC2MeZUV9kzwCjszntbgBuNMbmu5x4BbsZOy73HGDPHVX4B8CIQCLxhjNEk/J7iK3tYVKfTWRDS3M6K0mAB2HUPWT++wylFh3giawjvPzaPQ4UlAPRs05IJp3XkrG7xDPaTAFGZpNgI/nP9AH7aso/HZ67jdx+sYOaq3fz7mlRCghz+nprHwXWfwJvnwX/Hwc3zICLG2Wv6ADEObXwvImcAh4CpFYLFSGC+MaZERJ4CMMY8JCI9gfeBQUBb4Gugq+tUm4DzgAxgKXC1MWZdddceMGCASUvTXIcN9uNLMO/P8MBW3/5jmHYTbF0A92/yzbEVD9i5v4AfN+fw45Z9/LR5L1OL7wfgzuYvcFpyHMM6xzK0cwzRDsxe8rayMsMbP2zl77M2MLJna16+NtUz02x/WQxvj7apy3/zRaNY2S0iy4wxlfat1aZlUS/GmIUiknhC2dwKDxcD5Xs6jgE+cE3J3SYim7GBA2CzMWYrgIh84Dq22mCh3CRrLbRo49uBAmxX1JpP7B9v4jBv18YjDh4tZtHmHBZs2ssPm3PYuf8IAHEtQrk2YS89d+zgwDlP8d0Z53i5ps4LCBBuPaMzIYEB/GXGOu5+bzn/vsYDAaPDELhsEnx8A3x6K4yf0qg/rDgWLGrhJuBD1/122OBRLsNVBrDzhPLBlZ1MRG7FdpfRoYOXd3NrLLy8h0VZmSHvSDH7Dhex/3AR+w4VHrt/uKiE83q0pn/HKKTLeRAYage6G2mwKCszrNt9kAWb9rJg416W/XKA0jJDi9AghnSO4eZhSQzrEkuX+ObIF3fB7uZEDb7W29X2qBuGJVFm4LEv13HP+z/z0tX9nA8YvS6Fg0/AnD/A3D/DBX939npeVKtgISLNAYwxbtlAQET+CJQA77rjfADGmEnAJLDdUO46b5NVWmxXb3dx9pPprtwjfLo8g735hRWCQhH7DhdxoKCI0rLKf5RBAcJ/Fmw91gc/rtM5BK6fARf8wzd283ODA4eL+H5zDgs27mXBpr3kHLJrYU9t15Lbz+zEmV3j6dch8vh/iAX7bSur7zUQ2sJLNfeem05PoswYHp+5nt998DMvXuWBgDHkTrux1OKXbfr3IXfU/Bo/VG2wEJE7gYeBCPtQ8oGnGrIgT0RuwA58jzC/DphkAhWT7Ce4yqimXDkpJx3Kih2dCbUqI5eb305jb34hLcOCiGkeSkxECB1jwkntGEl0RAgxEaHENA8hOiLk2OOoiGBKSg2fr8jknZ928NAnq1kd1onH+Yrd6xfRpqd/ti5KywyrMnJZsGkv323cy8qMXIyByPBgzkiO48yucQzvGlv92oKVH0DJ0Sa5aKzcLcPt7KTHZ65HZAUvXtnX2YF8EbuWJS8DZj8CLdvZLLWNTJXBQkT+BJwGnFVhzKAT8KKIRBtjHq/rxVwzmx4EzjTGFFR4ajrwnog8hx3gTgaWYHNRJYtIEjZIXAVcU9frqno4lubDmW6oeeuyuOf9n4mOCGHe/51Bcuu6fQoODYJrB3fkmkEdWLJtP9N+iKB4y2t89t5rpHUJ4fqhHTkzOc75qZQNtCv3CN+n7+X79Bx+3JzDgYJiRCAlIZLfjUjmzK5x9EmIrN3COGPsiu2EgXBKb+cr78NuGd4JY+CJWesR4AWnA0ZAIFz+Brw9Cj6dCC1OgfaDan6dH6muZXE9kGKMOVpeYIzZKiJXACuBaoOFiLwPnAXEikgG8CjwCBAKzHPld1lsjLndGLNWRD7CDlyXAHcZY0pd57kbmIOdOjvZGLO2Xt+pqpusNXbv4Zhkt5/6rR+38diX6+jTrhWvTxjQoFW4IsLgTjEM7nQWhZNP5+rsFUzemcuNG7JJjAnnuiEdGd+/vW9kLsVmbF28dR/fp+fwffpetuw9DEB8i1DO7h7PWd3iGd4ltn45l7b/YDMEX/qqm2vtnyae0YkyY/jHVxsQEZ6/IsXZgBHcDK7+wE6pfe9KuOVriOns3PU8rMqpsyKywRjTva7P+QKdOusG/x0H+bvhjh/ddsrSMsPjM9fx1o/bGdmzNS9e1Y9mIW6cPbL0TZh5H8W3LeKr7CimLtpO2o4DhAUHMLZfO64fkujxnEKlZYY1mXl8n76Xhek5/PzLAYpLDWHBAQxOimF4cizDk+Po2rp5wxPkfXwDbPkWfr+hUUzjdJdXv9vCU7M3MKZvW567oq/z6Uv2bbEBI7SlDRgRsc5ez43qO3U2U0RGGGO+OeFk5wC73VlB5WOKCmDXcujivkVuBUUl/O6DFcxbl8VNw5L448U93P9H2/1imPl7gjfNZPSZDzI6pS1rd+Xxzk87+OznTN5fspPoiBASopqRENWMdpHNSIgKdz22XyNC6zdBsLCklLyCYnKPFHPgcBFbcw7zQ3oOP27JIbegGIBebVty8+mdGJ4cS/+OUe5NUXEo284GG3SbBooT3HFWZwyGp2dvRIB/Oh0wYjrbVd5vX2JbGBNmNIqsyNX9ZdwDfCEiPwDlmx0PAIZh1zqoxmrBUzbNR+oEt5wuO/8ot7ydxprMPP46uhcTTkt0y3lP0uIUaD/Ybn955oMA9Grbiicv78MjF/bg8xWZbMzKJ+PAETbsyefr9dkUnbCbW1R4cIUAYoNIaFAAuUeKyS0oJregyH49Yr/mucqPFJ+8H1jrlqGc26M1w5NjGdYlltjmoc583wA/vwNlJTBAt5qpzJ1ndcEYeGbORgJEeGZ8irMBo/1AO4bx4fV2DOOKqX6/BqPKYOEaRzgVO6BcPsq5ELit4jiGamSy1tqc/f2ud8uahU1Z+dz41lL2Hy5i0vUDOLdnazdUsho9R9s57/u3Hpezp1V48ElBqqzMkHO4kIwDR1y3gmP3N2bl882G44NJcKAQGR5CZLNgIl1B5dR2wUSF/7olaKRrn+lTWoXROS7CM3svlJXa3fASh0Os+8eYGou7zu5CWZnhn/M2gcAz4xwOGD1GwQVPwuyH7CypC5/y62nd1c2G6gK0NsZMPqF8mIjsKd9mVTUiZWUw43d2T+vzHmvw6RZtzuG2/y4jLDiQj24bSu8ED+yV3f0SGyzWfwnD7qn20IAAIb5FGPEtwkjtEHXS8+XBpLjUEBUeTLPgQO9tvFOdLfPtPP9z/+rtmvi8345IxgDPzdtEgAhPX97H2RlzQ26HvJ32A1jbftD36ppf46OqmxrwAnCwkvKDrudUY7PsLchYaueMN3DbyGnLMvjN5CW0aRXG53cN80ygAIjqaDepWT+jwacqDybtIpsRHhLkm4EC7MB+RLwNlKpG94xI5t5zk5m2LIM/fLYap/LjHXPe36D9EJjzCBza6+y1HFRdsGhtjFl9YqGrLNGxGinvyN8DX//Vbkzf58p6n8YYw3NzN3L/xysZ0imGaXecRrtIDw+49hhlN/052ATmYeTuhPQ5kHq97udRB/ee25W7zu7MB0t38tqCrc5eLCAARr8ERYdtl5Sfqi5YRFbznE63aGxmP2JX/l78fL37VQtLSrnvo5W8NH8zVwxI4K0bB9IyzAvrG3q4Vs9u+NLz1/a05VPtYjw3TUZoSu4f2Y1RKW15es4G5qzd4+zF4rrBGQ/YVCwb/XM7oOqCRZpr+9TjiMgt/Do7SjUG6fNg7adwxv0Q26VepzhSVMrNU9L47OdM7h/Zlacu7+OZNNGViesGsd3sHheN0eEc2PgVfPMYLH0dks+z3W+qTkSEZ8b1oU9CJPd+sII1mXnOXnDYvRDfE2beB0cr6+H3bdUtymsNfIbdqKji1NkQYKwxxuFQXH+6KK8OigrglcF2+83bf4Cguk/vPFJUyi1Tl7Joyz6eGZfCuP4JDlS0jr75G/zwPNyf7vsp1qtTWuzaSzvNjidlLIUD2+xzEmjTeoz+F7Tp4916+rHsg0cZ87JdfPrFXcOIb+ngvt4ZafDGuTDwFrj4WeeuU0/1WpRnjMkCThORs4HybHIzjTHzHaij8paFT9uZNDfMqneguPntpfy0dR//HJ/CZak+ECjAjlt8/yxs+gr6Xeft2tTewV2/BoWdS2H3Cts9CND8FDt/f8CNNv9Tm76NYrGXt8W3DOONCQMY/9pPTHxnGR/eOsS5Pb0TBsDg2+F/r0HvcXZPDD/h2E553qQti1rKWgv/OQP6XAWXvlznl/tsoADbj/9CH2jdE675sObjvamk0M5oWvyKnWYJEBhig0HCQPsPJmEgtErw63n6vm7O2j3c/t9lXNy7Df+6up9zs98KD8ErQ+1K+9u/r9eHNKd4Zac85ePKymDGvXZNxci/1fnlR4pKuWnKUhZv28dzV6Qwtp8PBQqw/1R7jLJ9+oX5vrm3gzF2rOjrv0LuDruobuhdkDAITjnVp/6JNAXn9zqFB8/vzlOzN9Alvjn3ntu15hfVR2hzuOR5ePdy+P6fcPYfnLmOm/nnbu2q4ZZPsdNLRz5R5zUVBUUl3DRlKf/z1UBRrudoKC2C9Lk1H+tp23+A18+x+4eHtoDrPoUbvrQb5yT010DhJbef2YnLUxN44et0Zqzc5dyFks+1U9S/fw6y/GOXaA0WTVF+Fsz7i/0km3JVnV5aUFTCzVPSXIGir+8GCrCf0Ju3trmifEX2BnjvKphyMRzKsunEb1sIXUZ4u2YKO0Pq75edyqDEaO7/eCUrduY6d7Hz/wFhLWH6b23KFh+nwaIpmvMIlByBS16oUx/48S2Kvlzar13NL/KmgACbiTZ9HhQf8W5d8vfA9Hvg1aGw40cY8Sj8dpnd/tTPE8w1NqFBgbx6XSrxLUOZODWNXbkO/e5ExMAFT0FmGix53ZlruJEGi6Ym/Wu7MGh43dZUlAeKJdv28/yVfhAoyvUYBcWH7T4P3lCYD9/+HV7qByvesynE71kBw+/TVOI+LKZ5KG9OGMjRolJueTuNw4Ulzlyo9zjocp5dM5P7izPXcBMNFhWVlcH8J+z0xcaoqMAuCIpJhtPvrfXLTgwUY/r6SaAA29UW1sotuaLqpLTYznB6KdWmfO96Pty9BC580r/XfTQhXVu34F/X9GPDnoP834crKCtzYOaoiB3sBjvhxIdnp2qwqGj/Vlj8Krx5vt3tqrFZ+LSddXPJ87UeQC0oKuHGt/w0UAAEBkO3i2DjLPsP3Gn7tsDi1+zUyJn3QUwXuOUbGD/luJTpyj+c1S2eP1/Sk7nrsnh6zkZnLhLZHs59FLZ8A6s/duYabqDBoqLYLnDDDNtt8eZI2L3S2zVyn6x1sOhf0PdaSBpeq5eUB4ql2/00UJTrMRqO5toZSO5WfMSOicx6AF7sC/9KtcniAkPgqvfhxll2nYTyWzeclsi1gzvw2oItTFuW4cxFBt5iJ2R89ZBN5+KDNFicqG0/uGmO7U+ecokz/2A8rawMvrzX7gl8Xu3WVBQUlXBDYwgUAJ3PhuAI9+WKKm89/PdyeCoR3h0Hy9+B2K5w0bN2TOLORdD9Il1E1wiICH8Z3YthXWJ45NNVLN2+3/0XCQi0mWkL821STx/kWLAQkckiki0iayqUjReRtSJSJiIDTjj+ERHZLCIbReT8CuUXuMo2i8jDTtX3OLHJNmC0aAPvXAYbZnnkso5Z/jbs/B+c/0St+stzC4qYMHkJadv388JV/fw7UIAN/Mnn2Q2R6jNFsagANs2Bmfcf33o4sAMG3GTXSDy0Ha79CAZNhOgkd38HysuCAwN45Zr+tI8K59apaSz/5YD7LxLfA4b/HlZ/ZFurPsaxdB8icgZwCJhqjDnVVdYDKAP+A9xvjElzlfcE3gcGAW2Br4Hy5ZObgPOADGApcLUxptpVLG5L91GwH94dD7t+tsna+l3b8HN62uEc+8/tlD524/gaPuluzznMjVOWknngCM9dmcIlfdp6qKIOWz0NPrkZLnwGmsfbvQWKC47/elKZ637OJigthKBm0OlM6HKuvWlQaHJ27DvM9W8uYc/Bozw7PoXRKW7++ygptCl4Cg/BXYs9nnnAK+k+jDELRSTxhLL1rgqdePgY4ANjTCGwTUQ2YwMHwGZjzFbX6z5wHeuZJY/h0fCbL+DD6+CLO+HIfjjttx65tNv88Lxt2l70bI2BYsm2/dz6ThoCvDtxMAMTG7Zbnk9JHgnB4fDVA5U/HxxubyHhENL81/vNouyGUF1GQMdhEOxgRlLl8zrGRPD5XcO4/Z1l3PP+z2zJPsS95ya7L49UUKj9YPrmSJj/uN2320f4Sm6odsDiCo8zXGUAO08oH1zZCUTkVuBWgA4dOrivZqHNbSK6z26DuX+Cgn12QZU/9EXnZdjFPilXQ3z3ag/97OcMHpq2moSoZky+YSCJsREeqqSHhLW0KdgL9tsgEBwOIRH2FtTMLuBTqhaiI0J455ZB/PGzNbz4TTpb9h7i2fEp7stU236Q7c7833/g1HE207APaDR/IcaYScaYAcaYAXFxcfU9B+/8tJ28ghOmWAaFwuVv2v7pH56HGff4xfJ8FjwNpgzOqnqoxxjDc/M28X8friS1YySf3nla4wsU5WI62z+81r1sF1LzeBssNFCoOgoNCuSZcX14+MLuzFy9mysnLSY7/6j7LjDi/0FELCx60X3nbCBf+SvJBNpXeJzgKquq3BFb9h7msS/Xcflri9i5v+D4JwMC4eLn7NaIy6fCxxOg2I2/HO62bwv8/F8b4CIrb2kdLS7l3g9X8NI36Yzrn8DUmwYTGa77OCtVGyLC7Wd25tVr+7NpTz6X/vtH1u1y0w54oS2g56V2oLvwkHvO2UC+EiymA1eJSKiIJAHJwBLsgHayiCSJSAhwletYR3SJb87bNw0i6+BRxr6yiFUZuccfIALn/MkmAFs/A94bb8cDfNG3f7ctouG/r/TpfYcKue6N//HFil08cH43nhnXh5AgX/l1UMp/XHDqKXx8+1DKDIx7bRFfr8tyz4l7jbUbX6XPcc/5GsjJqbPvAz8B3UQkQ0RuFpGxIpIBDAVmisgcAGPMWuAj7MD1bOAuY0ypMaYEuBuYA6wHPnId65jTOsfy6R2nERoUwJX/Wcw36yv5wQ+9E8b+B7b/CG+P8r1FNHtWw5ppdkeuFq1Penpz9iEbDDPz+Pc1/bjr7C7ObfSiVBNwartWfHH3MLrEN2fiO2m8vnArDZ5p2mGIzZq89nO31LGhdKe8KmTnH+XmKWms3ZXHX0f34vqhiScftHG27Y5q1R6u/8wu2/cF710FOxbBvSvtbJ4KFm3O4fb/LiM4MIDXJwwgtUNUFSdRStXVkaJSfv/xCmat3sOVA9rzt0tPbViLfeb98PM78MAWO9nGYdVNndV+hyrEtwjjw9uGcE73eP78xVr+Pmv9yYnEul1gg8ShbDvVzRc2Mdm5xO47PeyekwLFR2k7+c3kJbRuGcbndw3TQKGUmzULCeTfV6fy23O68GHaTn4z+X/kFhTV/4S9LvWZrigNFtUIDwniP9cP4DdDOzJp4Vbufn85R4tPmAXV8TSb/8eUwVsX2E/03mKMTXUcEWe7oFzKygxPz97Ag9NWMbRzDNPuOI320eHeq6dSjVhAgPD7kd14/soUlu/I5dKXf2TL3noOUncYChHxPtEVpcGiBoEBwl9H9+KPF/Vg1uo9XPvG/9h/+IRPCqecCrfMsz/UqZd6Ph12ua3fwvbv7YwtV5M1t6CIu99fzivfbeHqQR2YfMNAWjUL9k79lGpCxvZL4L2Jg8k/WsLYl3/kmTkb2LCnjrOlAgKh5xg7K6rosDMVrSUds6iDWat3c++HK2jbKowpNw46eT1CwX547wrIXGZXTA+82e11qJIx8PrZdrD9t8sokWDeW/ILz83bxMEjxTx8YXcmDu+kA9lKedjO/QX8+Ys1fJ+eQ2mZoWvr5ozq05ZRKW1rt6Zp+w92G95xb8Gplzla1+rGLDRY1NGyHfu55W177jcmDKB/xxNSYhQVwLQbYdNsOONBOPsPnlntvX6GTUsy5mUWtbiAv85Yx8asfIZ2iuHR0T3pfkpL5+uglKpSzqFCvlq9mxkrd7PElbm2T0IrRvVpy8V92tA2soqdE8tK4Z/d7eyoK99xtI4aLNxsW85hbnxrCbvyjvLClX25qHeb4w8oLYEvf2cXxaX+Bi5+HgIdzKxSVgqvnkZxSTG/i3qNWev2khDVjD9d3IPze52irQmlfMyu3CPMXLWbGat2sSojD4BBidGMSmnDhb3bENv8hM3JZv4efn4XHtxisw44RIOFA/YfLuKWt5fy885c/nBhD24ZnnT8P2Vj4NsnYOEz0PVCGDfZ5iRyQGHau4R+eSf3lN7LPIZy19mduWV4J/flqlFKOWZbzmG+XLmL6St3kZ59iMAA4bTOMYxKactZXeOIbxkG276Hty9xvCtKg4VDjhaXct9Hdk71Od3juXpQB87qFkdwYIV5A0tet7uoJQy0CQnD3ZfJ1RjD9OXbGTBjJAfKwnmzx2QevKgnbVpV0ZxVSvksYwwbs/KZsXIXM1bu5hdXyqE2rcLol9CCZ3dexZE2g2h27X8JD3Gmp0KDhYPKygyvfLeZKYu2k3OoiNjmoYzt15bxA9rTtbUrF/26L+CTiRCVCNd94pbFe6sycvnL9LX0zPyYx4PfIv28KSQPG9vg8yqlvM8Yw+rMPJZuP8CKnbms3JnLLQdfZnzgAgYUvUb71nH06xBJ3/aRpLSPJDm+BYEBDe9u1mDhAcWlZSzYuJePl+3km/XZlJQZUhJaMa5/AqNT2tEqewm8f7XtirruE5v5tB6y84/yzOyNfLwsg3YRhq+DfkdY62Tkxq/8I226Uqpe8tbNp9VHY5nR7R98fGQAK3fmknfEZsiOCAmkd0IrUtpHMigxmhE9Tk7zUxsaLDxs36FCvlixi4+XZbB+90FCggIY2bM1N3QpoP/3tyBFBXD1e5B4epXnKCsz7D54lK17D7Et5zBb9x5ma85hlm3fT1FpGTcOS+K+8FmEffcY3PiVXRyolGq8ymdFdRwKV0zFGMP2fQWs2HmAFb/ksmJnLut2HyQlIZJpd9Tv/4EGCy9ak5nHtGUZfL4ik9yCYlJa5DM58B9EFe8mYPRL5HUZy5Z9BWzbe5itOb8Ghm05hyksKTt2noiQQJLiIuh+SkvuPKsznVqUwgt97FjIddO8+B0qpTzmy/tgxXtVzooqLCll36Giqqfh1kCDhQ8oLCll/vpsPl6WwcpNW/lP0LMMCNjElrI2vFV6AZ+UDqcooBkdosNJio2gU2wESXERdIptTqe4COJbhB4/22r+E7Dwabh1AbTt67XvSynlQeWzosZPsSnM3UyDhY/JPniUL5Ztp8XWWZyTO434/LWUhraC1AkEDr615gHwQ3vhxRRIPg+ueNszlVZKed+xrqjTHPnbry5Y+Moe3E1KfMswJp7dHc7uDub/YOcSAhe/Aov/DYtfhp6jYchdVe+9+8PzUHLEbsSklGo6AgKhxyhY+b7NFeXgAr2TLu2xK6nKiUCHwfZTwu9W2o2VNs+HN8+F10fA6mlQWmFP8LwMWPoG9L0GYpO9V2+llHf0uhSKCyB9rkcvq8HCl0R2gJGPw33rbCLCIwfgk5ttl9MPz9tEhQueAgyc+ZC3a6uU8oaOw+w2BB5OW67dUL4otDkMmggDbrafHha/DF//Bb57CkqL7HORHbxdS6WUNwQEQo/Rrq6oAsfSCJ10WY9cRdVPQIDdjW/CDLj9R+h9OcT3hOG/93bNlFLe5IWuKG1Z+ItTToUxL3u7FkopX1DeFbXucxs4PMCxloWITBaRbBFZU6EsWkTmiUi662uUq1xE5CUR2Swiq0QktcJrJriOTxeRCU7VVyml/Eb5rKhNc2xXlCcu6eC5pwAXnFD2MPCNMSYZ+Mb1GOBCINl1uxV4FWxwAR4FBgODgEfLA4xSSjVpPS/1aFeUY8HCGLMQ2H9C8RigfCXJ28ClFcqnGmsxECkibYDzgXnGmP3GmAPAPE4OQEop1fRU7IryAE8PcLc2xux23d8DlKdGbAfsrHBchqusqvKTiMitIpImIml79+51b62VUsrXBAZ5tCvKa7OhjM0z4rZcI8aYScaYAcaYAXFxce46rVJK+a7yrqjN8xy/lKeDRZarewnX12xXeSZQMSFSgqusqnKllFIdh0F4rEcW6Hk6WEwHymc0TQC+qFD+G9esqCFAnqu7ag4wUkSiXAPbI11lSimljnVFzXa8K8rJqbPvAz8B3UQkQ0RuBp4EzhORdOBc12OAWcBWYDPwOnAngDFmP/A3YKnr9pirTCmlFNhU5R7oinJsUZ4x5uoqnhpRybEGuKuK80wGJruxakop1XhU7IrqOcaxy2i6D6WU8mcVZ0UVH3HsMhoslFLK3/W6FIoPQ7pzXVEaLJRSyt91PB3CYxxdoKfBQiml/F1gkE1bvnG2Y11RGiyUUqoxcLgrSoOFUko1Bg53RWmwUEqpxiAwCPpeA6EtHDm9bn6klFKNxcjHHTu1tiyUUkrVSIOFUkqpGmmwUEopVSMNFkoppWqkwUIppVSNNFgopZSqkQYLpZRSNdJgoZRSqkZi9x1qXERkL7CjAaeIBXLcVB1/pu+Dpe+Dpe+D1Zjfh47GmLjKnmiUwaKhRCTNGDPA2/XwNn0fLH0fLH0frKb6Pmg3lFJKqRppsFBKKVUjDRaVm+TtCvgIfR8sfR8sfR+sJvk+6JiFUkqpGmnLQimlVI00WCillKqRBgullFI1apLBQkQSRWRNLY+NFpF5IpLu+hrldP08pY7vw19EJFNEVrhuFzldPyfV8XsfLyJrRaRMRAac8NwjIrJZRDaKyPnO1NY57ngfXOc4UuF34zXnauyMOr4Pz4jIBhFZJSKfiUhkhef8+vehOk0yWNTRw8A3xphk4BvX46bqeWNMX9dtlrcr40FrgMuAhRULRaQncBXQC7gAeEVEAj1fPY+p9H1w2VLhd+N2D9fL0+YBpxpj+gCbgEeg8f8+NOVgESgir7s+Kc0VkWZVHDcGeNt1/23gUo/UznNq+z40RrX63o0x640xGyt5agzwgTGm0BizDdgMDHKywg5p6PvQWNT2fZhrjClxPVwMJLjuN5bfh0o15WCRDLxsjOkF5AKXV3Fca2PMbtf9PUBrD9TNk2r7PgDc7Wp6T24k3XF1+d4r0w7YWeFxhqvM3zT0fQBIEpGfRWSBiAx3a+08pz7vw03AV677jeX3oVJNOVhsM8ascN1fBiTW9AJjF6U0toUptX0fXgU6A32B3cA/na6YB9T5d6CRauj7sBvoYIzpB9wHvCciLd1XPY+p0/sgIn8ESoB3na2Wb2jKwaKwwv1SIKiK47JEpA2A62u20xXzsFq9D8aYLGNMqTGmDHidxtG8ru3vQFUygfYVHie4yvxNg94HV7fLPtf9ZcAWoKv7qucxtX4fROQG4BLgWvPryubG8vtQqaYcLGprOjDBdX8C8IUX6+I15QHTZSx2sLOpmw5cJSKhIpKE7cZY4uU6eZyIxJUP5IpIJ+z7sNW7tXKOiFwAPAiMNsYUVHiqUf8+1PWTVFP0JPCRiNyM3SPjCi/Xx1ueFpG+2G647cBtXq2NB4nIWOBfQBwwU0RWGGPON8asFZGPgHXY7oi7jDGl3qyrk6p6H4AzgMdEpBgoA243xuz3YlWd9m8gFJgnIgCLjTG3N/bfB80NpZRSqkbaDaWUUqpG2g3lIiIvA8NOKH7RGPOWN+rjLU35fWjK33tF+j5Y+j4cT7uhlFJK1Ui7oZRSStVIg4VSSqkaabBQSilVIw0WSimlavT/AVvvlEyO0PIMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aG1cni+lALNAXyAFecNSJlVIzlFIDlFIDwsLCHHVaz7D9K6goO3sTFJhk4S0y1gICkWepRbrK6eGzJll4O5cu96GUOr0LuYi8A3xt+zEbiKxyaIStjLOUG5VSvoDgWGjf5+zH+QdBYLhJFp4uIwna9tD/nlYLDNX7aJhk4fVcWrMQkaobA08BKkdKLQSuFpFmIhIDxAPrgPVAvIjEiIgfuhN8oStjdnv5ByF9pa5V2DM5KyTOzOL2ZOVlepVZd+ivqBTWzeWrzxqu57SahYjMAUYDoSKSBTwBjBaRvoAC0oHbAZRSW0VkLrANKAPuVkqV285zD/A94APMVEptdVbMHmnbl6Aqam+CqhQSq5e1NjzTwRQoKXCP/opK4QmQPFePiLJqNrnhdE5LFkqpa6opfu8sxz8FPFVN+SJgkQND8y5b50F4D/0f1h4hcXDyMJw6Ds1bOzMywxky1+r7yMHWxlFV5Yio/Bxo1cHqaAwnMTO4PVlelm6/7nmp/a+pXCPKLPvhmTKSoFUEtI6s/VhXqRwRZWZyezWTLDzZ1vn6vj7JwvRbeB6l9Mxtd+qvALNGVCNhkoUnS/kCOvTTaz7ZKzgGEJMsPNHxDN3U427JokWYbUSUqVl4M5MsPNXRPbB/s/0d25WaNoPWUWb4rCc6vXigmyULMCOiGgGTLDzV1nn6vseUur/WLCjomTKS9DLz4W64PFpYV7NrnpczycJTpczTwyeD6rbpOqCHzx7dY/5je5rMtRA5yD33UA/vprfzzc+xOhLDSUyy8ESHtsOhbXVvgqoUEgcl+VBwyLFxGc5z6pj+N490wyYo0DULMDO5vZhJFp4oZR5IE+hezwV4zRpRnidznb53x/4K0H0WYPotvJhJFp5GKT0KKnoEtAiv3znMftyeJyMJmjTVe1i4o8BQaB5sahZezCQLT5Pzi55QV98mKICgSPDxM8nCk2Ss1QtF+gVYHUn1RGy75plk4a1MsvA0KV/ob5jdLq7/OZr42PbjNnMtPEJZMWRvdK/1oKoTlqCboczACa9kkoUnUUrP2o4dBwHBDTuXGT7rOfZv0Vvmumt/RaWwBNuIqANWR2I4gUkWniRrPeRlNqwJqlJILOTuhYryhp/LcK5M22Q8dx0JValyMUszk9srmWThSdJW6PsuFzT8XCFxUFGql5Aw3FvGGr25VQs33wHSrBHl1WpNFiJyqYjsFpE8ETkhIvkicsIVwRlnOJAMbWIcs7S4WX3WM5xePNDN+ysAAsP0iCiz+qxXsqdm8SwwSSkVpJRqpZRqqZRq5ezAjGrkJEP73o45l1l91jMc2Q2nct2/vwL0iKiwBDMiykvZkywOKqXMVwWrFeXBsTRo56BkERim1xkyndzuLSNJ33tCsgDdb3HYjIjyRvbslLdBRD4FFgDFlYVKqXnOCsqoxoFf9X37Po45n4htjSiTLNxaxhq9/HdlTdDdhXXTX2zyD0Cr9lZHYziQPTWLVkAhcD5wse02sbYXichMETkkIinVPPdXEVEiEmr7WUTkVRFJFZFkEUmscuyNtj6T3SJyo72/mNfJSdb3jqpZgBk+6wkyknR/hafsbW3WiPJatdYslFI31/PcHwCvA7OqFopIJDrxVB2GMwGIt90GA9OBwSISDDwBDAAUsFFEFiqljtUzJs91IBlatIOWbR13zpA4+PVzKC0CX3/HnddwjPyDuulx4K1WR2K/cNsaUYd3QOwYa2MxHMqe0VARIjLfVks4JCJfiEit62IrpVYAudU89RLwMPrDv9JkYJbS1gCtRaQ9cAHwg1Iq15YgfgDG2/F7eR9Hdm5XCo4FlP5AMtyPp8yvqCowDJq3MSOivJA9zVDvAwuBDrbbV7ayOhORyUC2UuqXM57qCGRW+TnLVlZTeXXnniYiG0Rkw+HDh+sTnvsqLdLf1BzZBAVm9Vl3l7EGmvo7rp/KFUR0v4WZa+F17EkWYUqp95VSZbbbB0CdZweJSADwGPCPur7WHkqpGUqpAUqpAWFhbj55qa4ObQNV7viahUkW7i1jDXQcAE39rI6kbsIT9CxuMyLKq9iTLI6KyHUi4mO7XQccrce1YoEY4BcRSQcigE0i0g7IBiKrHBthK6upvHE54ITObQD/IAgMN8nCHZWc1CsMRw22OpK6C0vQI6IKDlodieFA9iSLW4ArgQNADnA5UOdOb6XUr0qpcKVUtFIqGt2klKiUOoBu5rrBNipqCJCnlMoBvgfOF5E2ItIG3TH+fV2v7fFyfoFmQdAm2vHnDokzE/PcUdYGXZv0hJnbZ6pc9sP0W3gVe0ZD7QMm1fXEIjIHGA2EikgW8IRS6r0aDl8EXAikoofp3my7dq6IPAmstx33b6VUdZ3m3i0nGdr1cs7wyZBY2PWd489rNEzmWkAgYqDVkdRd1TWizIgor1FjshCRh5VSz4rIa/x+5BIASqn7znZipdQ1tTwfXeWxAu6u4biZwMyzncurVZTDwa0woL4jmGsREgcnD+tmA/8g51zDqLuMJGjbwzHrgLlai3A9IsqsPutVzlazqPyX3uCKQIwaHNkNZacc319RqeoaUR0Tz36s4RoV5ZC5HnpfaXUk9VO5RpTZj9ur1JgslFJf2R4WKqU+q/qciFzh1KiM31R2bjtr+KRJFu7n4FYoyffM/opKYQl6oy6lPGf2uXFW9nRwP2pnmeEMOb/osfahXZxz/uAYQMyIKHeSYZuM5ymLB1YnvBsUHTcjorzI2fosJqA7nTuKyKtVnmoFlDk7MMPmQDKEdwcfe9Z8rIemzaB1lEkW7iQjCVp1hNaRtR/rrqquEdWynbWxGA5xtprFfnR/RRGwscptIXoZDsPZlHLOMh9nMgsKuo/Tmx15cK0C9CxuMP0WXuRsfRa/oCfQzVZKlbowJqPS8QxdlXdW53alkDjIXGfal91BXibk7/fs/grQI6L8W5sRUV7Enj6LaBH5XES2icjeypvTIzOc37ldKSROd6gWHHLudYzaVfZXRHrgzO2qRHS/hVkjymvYu5DgdHQ/xRj0kuP/c2ZQhk1OMkgT3WfhTCGd9b1pirJeRhL4tdRzLDxdWIKexW3WiPIK9iSL5kqpJYAopfYppf4JXOTcsAxA1yxCu4BfgHOvc3r4rEkWlstYC5GDoImP1ZE0XFiCbkbNz7E6EsMB7EkWxSLSBNgtIveIyBSghZPjMsDWue2C5amDIsHHzyQLq506plcY9vT+ikoxI/X9ti+tjcNwCHuSxf1AAHAf0B+4Dmi825u6yskjuqPT2Z3boL/FBnc2CwpaLXM9oDxzpdnqtO0OHfrB5v+ZpigvcNZkISI+wFVKqQKlVJZS6mal1GW23ewMZ8qx7Q/l7GGzlczwWetlroEmTaFjf6sjcZy+U+Fgym9/z4bHOmuyUEqVA8NdFItRVeV/rna9XHO9kFi9vWpFuWuuZ/xRxhrd7OgXaHUkjtPrcvBpBls+tjoSo4HsaYbaLCILReR6Ebm08ub0yBq7A8l6ZnXzNq65XkgclJfocf6G65UVQ/ZGz9pv2x7N20C3iZA8V28PbHgse5KFP3pnvLHAxbbbRGcGZWDbw8JFTVBgRkRZLW0FlBV5/szt6vS7To+K2rnI6kiMBrBnwaF3lVKrqxaIyDAnxWMAFOdD7h7oc7Xrrll19dm4c113XQNO5MCXd0ObGIgda3U0jhczClpF6I7unqZRwlPZU7N4zc4yw1EOpOh7V9YsAsOgWStTs3C1smKYez0UF8DVs6GZF45Kb+IDfa+FPUshL8vqaIx6Otuqs+cAQ4EwEXmgylOtAC+YMeTGTi/z4cJkIaI7uU2ycK1vH4as9XDlLD3U1Fv1vRZWPAu/zIGRD1kdjVEPZ6tZ+KEn3zUFWla5nQAur+3EIjJTRA6JSEqVsidFJFlEtojIYhHpYCsXEXlVRFJtzydWec2NIrLbdmsc8ztykvU3/ZbtXXtdM3zWtTa8Dxs/gOEPQPfJVkfjXMExED0Ctsw2cy48VI3JQim1XCn1L2CIUupfVW4vKqV223HuD4DxZ5Q9p5TqrZTqC3wN/MNWPgGIt92modeiQkSCgSeAwcAg4AkRcdHwIAsd+EU3Qbl6BdiQODieaUatuELmOlj0kO4fGvt3q6Nxjb5TIXevXv/K8Dj29Fk0E5EZtprA0spbbS9SSq0Acs8oO1Hlx0Cg8ivGZGCW0tYArUWkPXrfjB+UUrlKqWPAD/wxAXmXsmK9+Jorm6AqhcQBSs+3MJznRA58ej0ERcBl73rHOlD26D5JL5K42axD6onsGQ31GfAW8C7Q4BlbIvIUcAOQh17FFqAjUHWAf5atrKby6s47DV0rISoqqqFhWufQdqgoc23ndqWQWH1/NFUvL204XlkJzL1Bj3i7fr7r5tG4A79A6DkFfv0CJjwDzVpaHZFRB/bULMqUUtOVUuuUUhsrb/W9oFLqcaVUJPAxcE99z1PNeWcopQYopQaEhYU56rSu56o9LKoTXCVZGM7x7cOQtQ4uecO7O7Rr0u96KD0JWxdYHYlRR/Yki69E5C4RaS8iwZU3B1z7Y+Ay2+NsoOqGwxG2sprKvVdOsq6qt4lx/bX9W0FguEkWzrLxA9j4Pgz7M/SYYnU01ogYCCHxZvkPD2RPsrgReAj4md/24d5Qn4uJSHyVHycDlRv0LgRusI2KGgLkKaVygO+B80Wkja1j+3xbmfc6kAztekITe/5pnCAkzqw+6wyZ63WHduxYGPeP2o/3ViLQb6ru5D5ivpR4klo/kZRSMdXcOtf2OhGZAyQBXUUkS0RuBZ4WkRQRSUZ/8N9vO3wRsBdIBd4B7rJdOxd4Elhvu/3bVuadKsr1hDwr+isqhcSaZOFo+Qfg0+ugVQe47L3G06Fdkz7XgPiY2oWHqbWDW0QCgAeAKKXUNFvtoKtS6uuzvU4pdU01xe/VcKwC7q7huZnAzNri9Aq5e3V7rhX9FZVC4uDkR1CUB/5B1sXhLcpKYO6NUHwCrvsCAhzRguvhWrbTQ4Z/maOHDTf25Okh7N2DuwQ9mxt0n8F/nBZRY+bqPSyqU3WNKKPhvntE71Mx+Q3dvGho/a7T263uqXUUvuEm7EkWsUqpZ4FSAKVUIeDi2WKNRM4venvTsATrYjDJwnE2zYIN78Gw+80CemfqMh4CQmDzR1ZHYtjJnmRRIiLNsU2gE5FYoNipUTVWB5L1/AYfX+tiCI4BxIyIaqisDfDNX6HzGBj3hNXRuJ+mftD7KtixCE4etToaww72JIsngO+ASBH5GFgCPOzUqBojpVy/h0V1mjbTmy6ZZFF/+Qf1DO2W7eHymaZNviZ9p0JFKfz6mdWRGHawZzTUD8ClwE3AHGCAUmqZc8NqhE5kw6lcazu3K5kFBRtm6b/1v+XVH5sO7bNp1xPa94UtZvkPT1BrshCRKehZ3N/YRkCVicglTo+sscmxzdy2umYBv821MKuD1t3JI5D8mf7W7Kr90z1Zv+vgwK+/De4w3JZdzVBKqbzKH5RSx9FNU4YjHUgGBNr2sDoSnSxK8qHgkNWReJ4N70N5MQy+w+pIPEPPy8CnGWw2cy7cnT3Jorpj7FmA0KiLnGQIjXePndJCzBpR9VJWAuvfgdhxENbF6mg8Q0AwJFwEv87VKy4bbsueZLFBRF4UkVjb7UX0kh+GIx1wg87tSqeHz5pkUSfbFkDBQRhyl9WReJZ+18GpY7BzkdWRGGdhTw3hXuD/gE9tP/9ADbOtjXoqzIW8TBh0m9WRaEERumngyC6rI7FEQXEZOw+cYHtOPjsOnKC4tIL2Qf60b92cdkH++nFQc1r5N0UqN6hSCta8qRfJix1r7S/gaTqPhlYddVNUY11g0QPUmiyUUieBR1wQS+NV2bnnLjWLJj4QOQh2fQ/n/8f1O/a5SEWFIvNYIdtz8tmec4IdtgSRkVt4+piW/k1p7uvD4YLiP/T3B/j5nE4eQ3xTuXf/ZtZ1f5yCXYfpFBJI59DA35KJUbMmPnqP7pUvwIn9eg0tw+3YszZUF+BBILrq8Uop8/XJUazcw6ImPS+Dr/+sR6pYufyIA23dn8emjOM6MeScYOeBfE6W6P28RCAmNJBeHYO4ckAECe1akdC+JR1bN0dEKC2v4HB+MTl5p8jJK+JAXhE5eUWnf+62/2PyVAA3borl1Ca9KHN4y2YMjQ1haFwow+JC6di6uZW/vnvrey2seE6vFzXir1ZHY1TD5TvlGdXISYZWEe41Jr/bJFj0IKR84fHJYs3eo7y2dDerU/VM4Vb+TUlo34orBkSS0K4l3dq3okvbljT3q3nynK9PEzq0bk6H6j7w87Lg5bVUDL2TpUMuJCeviF0H8lm95yirUo+wYMt+AKJDAnTiiA3lnNgQggP9nPL7eqTgztBpuN5ydfgDXlub9WT2JIsypdR0p0fSmB1Idr8P5MAQvVRFyjw4958e959XKcWq1CO8tiSVdem5hLZoxmMXJnBR7w50CPJ3bPPQuncARZPBt9M+qDntg5qTGNWGqwdFoZRi58F8Vqce5efUIyzcsp/ZazMA6N6+FcPidM1jUHQwgc0a+SDDflNhwZ2QsQY6nWN1NMYZ7Pnr/EpE7gLmU2VNKK/eV8KVSk7Ckd3Qww0Xmut5GSy4Q69zFDnQ6mjsopTip52HeHVJKlsyj9OulT//vLg7Vw+Kwt/XCctulJzUO+AlTNTLpJxBRHSTVrtW3Do8hrLyCpKz8/g59QirU4/y4c/7eGdlGk2bCImd2nB5YgQX9+lw1lqO1+o+WW8Qtfl/Jlm4IXuSxY22+4eqlCmg1g2QDDsc3Aoo96tZACRcqFfBTfnC7ZNFRYVi8baDvP7TblKyT9CxdXOemtKTy/tH0KypEz94kz+FouN2D5dt6tOExKg2JEa14Z6x8RSVlrMh/Rir9xzhx20HefiLZJ78ZhuXJUYwdXAU8W1bOi92d+MXqEdDpcyDCc+4x5wj4zR7RkNZsBl0I3J6Dws36tyu5B8E8efD1vlwwVNuuSBeeYXim19zeGNpKjsP5hMdEsCzl/dmSr+O+Po4eWtapWDNW/rfLmpIvU7h7+vD8PhQhseH8vAFXVmffoyP1+5j9toMPvg5ncExwUwd0onxPdrh19SirXZdqd/1etnyFc/Cef+2OhqjCntGQ/kCdwIjbUXLgLeVUqVOjKvxyPkFmgfrcebuqOelsONrvWdy9HCrozmtrLyCL7fs541lqew9fJK48Ba8fFVfJvZuT1NnJ4lKe5bCkZ0w5W2H9OmICINighkUE8w/Jhbz2cYsZq/N4L45mwlt4ccVAyK5dlAUkcEBDgjeTUUOgv43wepX9H4Xw+6v9SWGa9jTDDUd8AXetP18va3sT2d7kYjMBCYCh5RSPW1lzwEXo3fe2wPcbFtrChF5FLgVPeLqPqXU97by8cArgA/wrlLq6Tr8fu6vsnPbXTuQu4wH3wDdFOUGyaK4rJx5m7KZvmwPGbmFJLRryRvXJjKhZzuaNHHxe7j2LQgMd8pEspAWzbhjVCzTRnRmZeoR/rdmH28v38Nby/cwqksY1w3uxJiEcHxc/Ts7mwhc9CIUnYAf/qFrt/1vsjoqA/uSxUClVNU2kqUiYs8SkR8ArwOzqpT9ADyqlCoTkWeAR4G/iUh34GqgB9AB+NE2vwPgDeA8IAtYLyILlVLb7Li++ysvhUPb3XvROb9A6DoBtn0JE561bGOmUyXlzFmXwYwVezlwoojeEUH8/aL+nNutreuTBOhBCbsXw+hH9R4gTtKkiTCqSxijuoSRk3eKOesy+WRdBn+atYEOQf5cMyiK64Z0oo03DcNt4qNrayUF8NWfoVlLPdjCsJQ9yaJcRGKVUnsARKQzdsy3UEqtEJHoM8oWV/lxDXC57fFk4BOlVDGQJiKpwCDbc6lKqb22a39iO9Y7ksXhHVBe4p79FVX1vEzXLNKWQ9y5Lr30iaJSPkrax3ur0sg9WcKgmGCevbw3I+JDrZ0dvfZt3fk/4BaXXbJ9UHMeOK8L946NY8n2g3y8NoMXftjFjBV7uWN0LLcMi/GeUVRN/eCKD+Hjy2HeNPBrCV3OtzqqRs2eZPEQ8JOI7EXvvd0JuNkB176F39ab6ohOHpWybGUAmWeUD3bAtd2DO+1hcTZx50KzID1KxUXJIvdkCTNXpfFhUjr5RWWM7hrG3WPiGBjtBhMXTx2HLbOh5+XQItzll/f1acL4nu0Z37M9uw7m8+x3O3nu+53MSkrnL+d24fL+Ea7rt3EmvwC45hP48GKYez1cNw+ih1kdVaNlz2ioJSISD3S1Fe201QDqTUQeB8oAhy1iLyLTgGkAUVF/HO/ulg4k6/6AyiXB3VXTZnoZ6e1fw8SXnNrscvBEETNW7GX22gyKysoZ36Mdd4+Jo2fHIKdds842fwSlJ2GI9c2HXdq25N0bB7AuLZenv93OI/N+5d1VaTx8QVfO697W89em8m+lk8T7E2D2VXDTV9Chn9VRNUr27JR3N9BcKZWslEoGAmyT9OpFRG5Cd3xPVer00mzZQGSVwyJsZTWV/4FSaoZSaoBSakBYWFh9w3OtnGRo29Mth6T+Qc/LoDgPUpc45fSZuYU8Nv9XRjzzEx/8nM6EXu344S8jmX5df/dKFOVlsHYGdBrmVs2Hg2KC+eLOobx9fX8qlGLaRxu54q0kNqR7wdzZwBC4YQEEtIGPLoVDO6yOqFGyp656W+WIJQCl1DGgXmtp20Y2PQxMUkoVVnlqIXC1iDQTkRggHlgHrAfiRSRGRPzQneAL63Ntt1NRYRsJ5T4fOGfVeZQe4pvyhUNPu/NAPg98uoXRzy/j8w1ZXDEggmUPjubFK/sSF+6GE9J2LoK8DBhyp9WR/IGIcEGPdiz+80j+O6UXGbmFXP5WErfN2sDug/lWh9cwrTrA9Qv0AIuPLoFj6RYH1PjY02fhIyJSWQsQER+g1qEXIjIHGA2EikgWeivWR4FmwA+26vEapdQdSqmtIjIX3XFdBtytlCq3nece4Hv00NmZSqmtdfwd3dOxND3awx1nblfHx1cvx5D8qV7iwi+w3qeqqFAs332Y91amsSr1CM19fbh5aDS3jexM21b+DgzaCda+pZf16Hqh1ZHUqKlPE64dHMUl/Trw/up03lq2hwteXsEV/SP5y3ldaBfk5u9xTUJi4fr58P6FMOsSuOU7aNnO6qgaDVFnLtJ/5gF6bkQn4G1b0e1AplLKbdcRHjBggNqwYYPVYZxdyjz4/GaYthw69LU6GvukrYQPJ8Ll7+vJenV0qqSceZuzmLkqjT2HT9KulT83DO3EtYOiaB3gAUM/c36Bt0fqPT6G3mt1NHbLPVnC60tT+WhNOk1EuGV4DHeMiiWouTXDoBssawN8OAnadIKbvnGv1Zo9nIhsVEoNqPY5O5JFE3THceUwmB/Qk+Pcdrlyj0gWi/9P76z22H6ndhg7VEU5vNgdIgbA1faPTTh4oohZSenMXpvBscJSenUM4k8jYriwV3vnL8nhSPPv1PNNHtgGzVtbHU2dZeYW8sLinSzYsp/QFs3416QeXNirnWd2gu9dDh9fAe16wg1f6rkYRoM1KFl4IrdPFscz4M1zoNNQmPqZ1dHUzbePwIaZ8FCqHqlyFinZecxclcZXyfspq1Cc370ttw7vzMDoNp73AVVwCF7qAYk3wkXPWx1Ng/yalcej85NJyT7BuIRwnrykZ/X7dLi7Hd/Ap9fr4bTXfga+Htq85kbOliw86Gudl1AKFtqaMC56wdpY6qPnZVBerDt6q1FeoVi89QBXvZ3ExNdW8f3WA0wd3IllD47m7esHMCgm2PMSBegEWV7i3rPt7dQrIogFdw3j8Qu78fOeo5z34nI+WJ1GeYWHfXFMuAgueRPSVsDnt+gVEQynaeS7rVhg4/uwd5mer1DN/gduL2IABEXpUVF9rj5dnHuyhPmbs/koKZ30o4V0bN2cxy/sxpUDIz23bbxSWTGsf0+vwBsaZ3U0DtHUpwm3jezM+J7teHxBCv/8ahsLtuzn6ct6kdDu7DVGt9Lnar2O1LcPwXePenytz53ZlSxEpAWAUqrAueF4uWP7dF9FzCjo74hJ8BYQgZ5TIOkNKgqOsmp/BZ+uz+SHbQcpKa+gX1RrHrygK+N7tPOOWcSgl2g/ecgth8s2VGRwAB/ePJAvt+zn319vY+Krq7h9VGfuHRvvnM2inGHwNDi+D5Jeh67jXb4kTWNx1j4L2+S7R4BA9FIf+cAzSqk3a3yRG3DLPgulYNZkyN4IdyV5Zq3C5uDOtbSdcz5PN72TtwpG0DrAlyn9OnLVwEjP+lZqD6Vgxihdu7hrjfuuDuwAx06W8J9vtvPFpixiQgN5akpPhsaGWh2WfUqLYMZovRHVnT+bEVL1VK8+CxH5O3qm9WilVIhSKhgYA0ywPWfUxYaZeiG+85/0yERRXFbO18n7uf69tQz54DB7K9pzsU8Sr1/bj7WPjeOJi3t4X6IAvR90zi8w+HavThQAbQL9eOHKPvzv1sGUVyiufWctD3/+C8cLS6wOrXa+/jDlLTh5WG/NajhcjTULEdkJ9FFKFZ1R3hz4RSnVpdoXugG3q1kc2wfTh+r2/usXeNSHzo4DJ/h0fSYLNmdzrLCUjq2bc3n/CG4tnUOr9S/DAzugZVurw3ScsmI4mAL7N0P2Zt2/VFIAD2zXC9s1EqdKynllyW7eWbmXNgG+/OPiHlzcu737D05Y/hz89J96zwVq7M5Wszhbn4U6M1HYCk+JSIXDovN2FRWw8B5AYNJrHpEoSsoqWLA5m4/XZfBL5nF8fYTzu7fjqoGRDIsL1RvuHLoK1r2o5x0MnmZ1yPVTXqaXid+/yZYcNuk90Stso2oCQqBDIgya1qgSBUBzPx8emZDApD4deHReMvfN2cxnGzJ58Pyu9IlsbXV4NRv+F9j1LXzzgB6abmZ4O8zZahZLgP8qpZacUT4W+D+l1BgXxFcvblWzWP8ufPNXmPgyDHDvTu3isnLmbshi+k+p7M8rokvbFlw1MIop/ToSXN3mOtOHgV8LuPV71wdbH8f26WalyuSQkwxlp/RzzVrpmfQdEvWqph0TISjSI5K7s5VXKD78OZ1Xl+7meGEpYxPCuX9cvPsmjSO74a0REDMCrp1r/g3roF6T8kSkB/AlsArYaCseAAwDJrvzGk1ukyyOpcObQ/W+wtfPd9s/2qJSvQvd28v1LnSJUa25/9wujKxtg6EVz8PSJ+HPKdA6subjrHZ0Dyx/BpLnAkovC9++j04KlckhuDM08ZLRW06SX1TKrKR9vLNyL8cLSxnTNYz7z+1CX3dMGmvfhm8fhotfMduy1kG9Z3CLiD9wLXq7U9AL/X1cXfOUO3GLZFFRAbMmwf4tttFP7vdhWlhSxuy1Gby1fC9HCooZFBPM/ePiGRobYl/bdO5eeLUfnPckDLvP+QHX1bF9sOJZ2DJH72o36DY9Lj+0K/iYKUb1VVBcxoc/p7t30qio0KvTZm+EO1ZBcIzVEXmE+tYs4oC2SqnVZ5QPAw5UbrPqjtwiWbhx89PJ4jI+WrOPd1bs5ejJEobFhXDv2HiGdA6p+8lmjAFVAbcvd3yg9XViv671bJqla3MDboHhD3hXR7wbqEwa767cy7HCUkZ3DeP+cfH0i2pjdWhaXpau2bftATd97Rn7xlisvsnia+BRpdSvZ5T3QvdlXOzwSB3E8mThps1Plc0Ilf+5R3YJ476xcQxoyFalP78Oix+HezdZv+NfwSFY9ZKeba3KIfEGGPEgBHWs/bVGvRUUlzErKZ13Vui/q1Fdwrj/3HgS3SFpbJkDC+5w39qvm6lvslivlBpYw3O/KqV6OTBGh7I0Wbhh81PeqVLeX53GzFVpnCgqY1xCOPeOi3dMs0FeNrzUHcb+HUZaNL69MBdWvwzr3tFDX/tcA6MegjbR1sTTSBUUl/FR0j5mrNjjPklDKfj0Oti9WG8H0La7dbF4gPomi91KqfganktVSrntIjmWJot178CiB92iY62otJz3V6fz5rJU8ovKOL97W+4dG0+vCAdvUzpzgp45e1eSY89bm1PHIekNWDNdz4XodQWMfsT6Gk4jd7K4jFlVksa4hHAevKAr3dpbNGnz5BF4cwi0bA9/WgJNPWDvFIvUN1nMAZYqpd45o/xPwHlKqascHqmDWJYsKpufogbrTeYtan6qqFAs/GU/z32/k+zjpxiXEM5fz+9K9w5O+s9amSDvTHL+Nzel9NDIbQv0WkBFeXoHv9GPQng3517bqJOTxWV88HM6by/fw4miMib16cBfzutCTGj9d1mstx3fwCfX6trvWLMARU3qmyzaAvOBEn4/dNYPmKKUOuCEWB3CkmThJs1PSXuO8t9F2/k1O4+eHVvx2IXdnL++T8FheKELjPirc/4j5h/US6XsXaZvJ7J1edcLdZLwlK1pG6m8wlJmrNzDzFXplJRXcOWACO4dG+/6PTQW3AW/zIFbFkNktS3sjV5Dd8obA/S0/bhVKbXUzovORK8tdUgp1dNWdgXwT6AbMEgptaHK8Y8CtwLlwH1Kqe9t5eOBV9B7cL+rlHq6tmtbkixONz+9Cv1vdO21gdRDBTz97Q5+3H6QDkH+PDS+K5P7dKRJExfVbmZN1ps63bup4TWq4gLY97MtOfwEh7bp8uZt9Iq9sWOg82jTJ+FhDucX88ZPqcxemwEC1w/pxF2jYwlp4aKdIovy9ETSps3g9pWNbla+PSzZKU9ERgIFwKwqyaIbUIHez/vBymQhIt2BOcAgoAPwI1C59tQu4DwgC1gPXKOU2na2a7s8WeSm6bWfos6B675wafPTkYJiXvlxN7PXZdDc14e7xsRyy7AY1y8vvWmW3tRp2jI9ya0uysv0rOo9P+kEkbUOKsrApxl0Okcnhs5joF1vM3HOC2QdK+SVH3fzxaYsmvv6cOvwGP40sjOt/F2w70naCvjwYhh0O1z4rPOv52HquzZUgyilVohI9Bll220BnXn4ZOATpVQxkCYiqejEAZCqlNpre90ntmPPmixcSin46j4QH5j0qssSRVFpOe+tSmP6sj2cKi1n6uAo7h8X77pvaWdKmAhfPwArX9T7CZQW6ltJYfWPS09ByUn9OP8glOQDomdWn3OPrj1EDgZfD9zu0ziriDYBPHdFH24fFctLP+zi1aWpfJi0jztHx3LjOdE093PiF52YkTD4Tlg7HbpO0H9nhl3cZRprR2BNlZ+zbGUAmWeUD3ZVUHbZuUh/W7nweQiKcPrlKioU8zdn8/zineTkFXFe97Y8MiGB2LAWTr/2WQUE641nti/Ut0rSBHwD9Ye+X4BeasM3QD9u1UE/7hwM0cN1E5PZh6DRiAtvwRtTE7kzO4/nF+/k6W938N6qNO4bG8eVAyNp1tRJSePcJ2DPEvjybr33RfPWzrmOl3GXZNFgIjINmAYQFeWi/SLKSvTOd6FdXbLz3c97jvDUN9vZuv8EvSOCePmqvgyuz6xrZ7n0HT1r1jfAlhwC9TIbbjIp0XBPPTsG8cHNg1iXlstz3+/g/77cyus/pXLbiM5cMyiKwGYO/pjyba73vnj3PPj+cbjkDcee30u5S7LIBqoOH4qwlXGW8t9RSs0AZoDus3BCjH+0YSbk7tErWzpxraG9hwv47yLded2xdXNeubovF/fu4LrOa3v5NofQaqfmGEatBsUEM/f2c1iVeoQ3f9rDf77ZzmtLU7lxaDQ3D42mTXUrH9dXx/566fn178B5/4ZAN/rS5abcJVksBGaLyIvoDu54YB16K9d4EYlBJ4mr0QsbWu/UMVj+tO58jT/fKZc4XljCK0t281HSPvx9fXh4fFdrOq8Nw0VEhBHxYYyID2NTxjGmL9vDq0t2886KvVwzKIo/jYhx3JDbflN138W2+TDwT445pxdzWrKwTeobDYSKSBbwBJALvAaEAd+IyBal1AVKqa0iMhfdcV0G3K2UKred5x7ge/TQ2ZluszT6iuf1DOLz/+PwZpaSsgo+WrOPV5fsJr+olKsGRvHAeV0Ia2lR57VhWCAxqg3v3DCAXQfzeWv5Hj5MSuejNelc0rcjd4yObXg/XdueENYNkj8zycIOThs6ayWnD53N3QuvD4I+V8Fkx7V3KqVYvO0g/2/RdtKPFjIiPpTHL+rmnXtbG0YdZR0r5J0Ve/lkfSYl5RWM79GOO0fH0juidf1PuvIFWPJvuD8Z2nRyWKyeypJ5FlZyerL49HpIXQL3boRW7R1yypTsPP7zzTbW7M0lLrwFj1/UjdFdwtx/z2PDcLEjBcV8sDqdD5PSyS8qY3hcKHeNjuUce/dhqep4BrzcC8b+H4x80DkBexCTLBxpXxK8Px5GPwaj/9bg0x3IK+L5xTv5YlMWbQL8+Mt5XbhmYCRNfczkM8M4m/yiUj5em8G7K9M4UlBMl7YtmNIvgsl9O9StX2PmeL1y8d1rG/3IPZMsHKWiAt4dB/kH4N4NemhoPRWWlDFjxV7eXr6X8grFzcOiuXtsnGtmsRqGFykqLWf+5mw+25DJpozjiMDgmGAu7RfB+F7tav8/tf49+OYBuH2FnhTaiJlk4SjJn8G8P8El06Fv/QZllZVX8NnGLF76YReH8ou5qFd7/jY+gagQs06NYTTUvqMnWbB5P/M3Z5F+tBC/pk04r1tbpvTryMguYfg1rabGXpgLz8fD4DvggqdcH7QbMcnCEUpPwesD9WJ205bXeY0ipRQ/bj/EM9/tIPVQAYlRrXnswm4N26XOMIxqKaXYknmcBZuz+So5h9yTJbQJ8GVi7w5c0q8jiVGtf9+/MftqyNkCf9naqLdftWRtKK+z5k3Iy4RL3qxzoticcYz/t2gH69Jz6RwayFvXJXJBj3am89ownERE6BfVhn5Rbfj7xO6s3H2YeZuymbshk4/W7KNTSACX9O3IxX06EBsWiPS+AnZ9C+mroPMoq8N3SyZZ2KPgMKx8Se+fEDPS7pelHTnJc9/vYNGvBwht4ceTl/Tk6oGR+JrOa8NwGV+fJoxNaMvYhLbkF5XyXcoBFmzJ5tWlu3llyW4i2jTn3Lgo/t40ELXlU3xNsqiWSRb2WPZfKDullwWww5GCYl5dspvZazPwa9qE+8fFc9vIzrRw9Bo3hmHUSUt/X64YEMkVAyLJyTvFku2HWLbzMHN/OUJP1Z8LfpnHrYevZmhCR0Z1CSOhXUvTAmBjPr1qc2g7bPwABt5W67pHhSVlvLsyjbeX76GorIKrB0Zy/7nxhLf0d02shmHYrX1Qc64b0onrhnSipKyC3UlFtFyygvi81Tz9bW+e/nYHbVs1Y1SXMEZ1CWd4XChBAY13tKLp4K7N/y7Xm/Hct6XG5bPLyiuYuyGLl37cxeH8Yi7o0ZaHx7vBsuGGYdivohxe7AYRAzkw4T1W7DrM8l2HWbH7MPlFZTQR6BfVhpHxYQzpHEyfyNZet06b6eCur9QlkPqDXv/pjERRXqFYn57L91sPsHjrQbKPnyIxqjXTpyaaEU6G4Yma+EDPy2D9u7SbfIorB0Zy5cBIysor2JJ5nOW25PHSj7sA8GvahH6RrRkcE8zgziEkRrVx7sZNFjM1i5pUlMNbI6D0JNy9Dpo2o6i0nFW7j7B42wF+3H6I3JMl+DVtwvC4UK4aGMn53dua9k3D8GTZm+CdMXDxq9D/xmoPOV5Ywrq0XNam5bIuLZet+/OoUODrI/TqGMTgziEMjgmmf6c2tPSwSbZmnkV9bPwQvrqPk5Nn8qMM4futB1i28zCFJeW0bNaUMQnhXNCjHaO6hpmOa8PwFkrB6wOgZXu46Wu7XnKiqJSN+46xdm8u69KOkpyVR1mFoonojZ0GRQczpHMII7qEOm/3PwcxyaKODh85QosZg8igHRNPPk5pOYS1bMZ53dtyfve2DI0NrX4mqGEYnm/ZM7Ds/8FfUuq1VXJhSRmb9h1nXdpR1qTlsiXzOCVlFYS2aMb1QzoxdUgUoS3cc7sBkyzstP/4Ke6ZvYkx+2dwb9MF3OH/LFG9R3JBj7b0i2zjfjvTGYbheEf3wGuJcO6/YPifG3y6otJykvYe5cOf01m28zB+Pk2Y1LcDNw+LpkeHoIbH60Cmg9tOYS2b0Y6j3On3LSc6T2b61GmmD8IwGpuQWOg4AH79zCHJwt/XhzFdwxnTNZzUQwV8+HM6n2/M4vONWQyOCebmYTGc170tPm7+ZdS0pVTh69OEN9t9Q1OBVhP/YxKFYTRWva+EgylwcJtDTxsX3oInL+nJmkfH8diFCWQdO8Ud/9vIqOd+4t2VezlRVOrQ6zmSSRZVHUmF5E/gnLugdZTV0RiGYZUel4L4wK9znXL6oABfpo2MZflDo5k+NZEOQc35zzfbGfLfJTzxZQppR0465boNYfoszrTnJ+jYH/zNVqaG0aj97zI4vFNvuVrHxUPrIyU7j5mr0/j6lxxKyisYmxDOLcNiGBZXjx0A6+lsfRZOewdEZKaIHBKRlCplwSLyg4jstt23sZWLiLwqIqkikiwiiVVec6Pt+N0iUv3AZ0eKHWMShWEY0OtKvdJ05hqXXK5nxyBevLIvqx4Zw/3j4knOOs51761l/Msr+XR9BkWl5S6JoybOTJcfAOPPKHsEWKKUigeW2H4GmADE227TgOmgkwvwBDAYGAQ8UZlgDMMwnCrhIvANgGTnNEXVJLylP385rwurHxnLc5f3RgT+9sWvDH16KS8u3smh/CKXxlPJaclCKbUCyD2jeDLwoe3xh8AlVcpnKW0N0FpE2gMXAD8opXKVUseAH/hjAjIMw3C8Zi30tgTbFkBZiesv39SHKwZE8u39I5h922ASo1rz2k+pDHt6KQ/M3cLW/XkujcfVQ2fbKqVybI8PAG1tjzsCmVWOy7KV1VT+ByIyDV0rISrKdE4bhuEAva+ElM8h9UdIuNCSEESEobGhDI0NJe3IST78OZ25GzKZtymbwTHB3Do8hnHdnD/01rLRUEr3rDusd10pNUMpNUApNSAsLMxRpzUMozGLHQsBIU4bFVVXMaGB/HNSD5IeHcfjF3Yj69gppn20kbEvLOP91WkUFJc57dquThYHbc1L2O4P2cqzgcgqx0XYymoqNwzDcD4fXz2Mdue3UHTC6mhOC2ruy20jO7P8odG8OTWR0BbN+NdX2zjnv0v4f99uxxmjXF2dLBYClSOabgS+rFJ+g21U1BAgz9Zc9T1wvoi0sXVsn28rMwzDcI3eV0JZEeywb2FBV2rq04QLe7XnizuHsuDuYYxJCOdgXpFThto6rc9CROYAo4FQEclCj2p6GpgrIrcC+4ArbYcvAi4EUoFC4GYApVSuiDwJrLcd92+l1Jmd5oZhGM4TMRDaREPyp9D3WqujqVHfyNa8ek0/p9QqwInJQil1TQ1PjavmWAXcXcN5ZgIzHRiaYRiG/USg1xWw8gXIPwAt21kd0Vk5awKfWe7DMAyjNr2uBFUBKV+45nqlp2BfEpw67prr2cGsOmsYhlGbsC7Qvo+eoHdOtY0gDVNyEjLXQvpq2LcasjdCeQlEDNKbMDW1fv8LkywMwzDs0etKWPw4HNkNofENO1fRCchYoxPDvtWwfzNUlOnFCzv0hcF3QPPWsOTf8O3DcPErjvgNGsQkC8MwDHv0vAwW/13XLsY+bv/rKsp1c1LWOkhfpW8HknWzVhNf6JgIQ++D6GEQORiatfzttcUFsOpFaN8XBtzs6N+oTkyyMAzDsEer9hAzEjZ/pJuNSgt/u5UU6n6G0pP6vqTwt8dlVdZy8vHTo6tGPKiTQ8Qg8Auo+Zpj/64Ty6KHoG0PiBzk/N+zBmaJcsMwDHttWwhf3Ko/9H0DwLc5+AXqe98AffML+O1x5fN+LXTzUscB4Otft2ueOgYzxujEc/typ47GMntwG4ZheLKD2+Ddc6FdT7jxa2jq55TLWLKfhWEYhuEgbbvDJW/oEVPf/c2SEEyfhWEYhifoMQVyfoFVL+kO7/7O3wuuKlOzMAzD8BRj/w9ix8GiByFzfe3HO5BJFoZhGJ6iiQ9c9i606gCfXqeXH3HVpV12JcMwDKPhAoLh6tlQfALm3uiyXfxMsjAMw/A0bXvA5Dcgcw1894hLLmk6uA3DMDxRz0shZwusfkXP4Ui8wamXMzULwzAMTzXuCb316zd/hSznzi0zycIwDMNTNfGBy96Dlu1tHd4HnXcpp53ZMAzDcL7KDu+iPJh7g9M6vE2yMAzD8HTtesLk13WH9/ePOuUSliQLEblfRFJEZKuI/NlWFiwiP4jIbtt9G1u5iMirIpIqIskikmhFzIZhGG6t52V6qfOKcqiocPjpXZ4sRKQncBswCOgDTBSROOARYIlSKh5YYvsZYAIQb7tNA6a7OmbDMAyPcO6/4OKXoYnjP9qtqFl0A9YqpQqVUmXAcuBSYDLwoe2YD4FLbI8nA7OUtgZoLSLtXRyzYRiG+3NCkjh9aqeduWYpwAgRCRGRAOBCIBJoq5TKsR1zAGhre9wRyKzy+ixb2e+IyDQR2SAiGw4fPuy86A3DMBohlycLpdR24BlgMfAdsAUoP+MYBdRpow2l1Ayl1ACl1ICwsDAHRWsYhmGARR3cSqn3lFL9lVIjgWPALuBgZfOS7f6Q7fBsdM2jUoStzDAMw3ARq0ZDhdvuo9D9FbOBhUDlAu03Al/aHi8EbrCNihoC5FVprjIMwzBcwKq1ob4QkRCgFLhbKXVcRJ4G5orIrcA+4ErbsYvQ/RqpQCFwsxUBG4ZhNGaWJAul1Ihqyo4C46opV8DdrojLMAzDqJ6ZwW0YhmHUSvQXd+8iIofRTVn1FQoccVA4nsy8D5p5HzTzPmje/D50UkpVO5zUK5NFQ4nIBqXUAKvjsJp5HzTzPmjmfdAa6/tgmqEMwzCMWplkYRiGYdTKJIvqzbA6ADdh3gfNvA+aeR+0Rvk+mD4LwzAMo1amZmEYhmHUyiQLwzAMo1YmWRiGYRi1apTJQkSiRSTFzmOr3e7VG9TxffiniGSLyBbb7UJnx+dMdfzdr7BtAVwhIgPOeO5R25a/O0XkAudE6zyOeB9s5zhV5W/jLedF7Bx1fB+eE5Edtm2e54tI6yrPefTfw9k0ymRRRzVt99oYvaSU6mu7LbI6GBdKQa+OvKJqoYh0B64GegDjgTdFxMf14blMte+DzZ4qfxt3uDguV/sB6KmU6o3eXuFR8P6/h8acLHxE5B3bN6XFItK8huNq2u7VW9j7Pngju353pdR2pdTOap6aDHyilCpWSqWhV0Ye5MyAnaSh74O3sPd9WGzbEhpgDXqPHfCev4dqNeZkEQ+8oZTqARwHLqvhuJq2e/UW9r4PAPfYqt4zvaQ5ri6/e3Xs2vLXAzT0fQCIEZHNIrJcRP6wqrSHqM/7cAvwre2xt/w9VKsxJ4s0pdQW2+ONQHRtL6jPdq8ewN73YToQC/QFcoAXnB2YC9T5b8BLNfR9yAGilFL9gAeA2SLSynHhuUyd3gcReRwoAz52bljuoTEni+Iqj8upeW+PmrZ79RZ2vQ9KqYNKqXKlVAXwDt5Rvbb3b6Am3rLlb4PeB1uzy1Hb443AHqCL48JzGbvfBxG5CZgITFW/zWz2lr+HajXmZGGvmrZ7bVQqE6bNFHRnZ2O3ELhaRJqJSAy6GWOdxTG5nIiEVXbkikhn9Puw19qonEdExgMPA5OUUoVVnvLqvwertlX1JDVt99rYPCsifdHNcOnA7ZZG40IiMgV4DQgDvhGRLUqpC5RSW0VkLrAN3Rxxt1Kq3MpYnamm9wEYCfxbREqBCuAOpVSuhaE62+tAM+AHEQFYo5S6w9v/HszaUIZhGEatTDOUYRiGUSvTDGUjIm8Aw84ofkUp9b4V8VilMb8Pjfl3r8q8D5p5H37PNEMZhmEYtTLNUIZhGEatTLIwDMMwamWShWEYhlErkywMwzCMWv1/h9aClihTaGQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the first ten multistep forecasts\n",
    "# in the train set.\n",
    "\n",
    "# Each row corresponds to 24 hr forecasts.\n",
    "\n",
    "for i in range(0, 10):\n",
    "    tmp = pd.concat(\n",
    "        [\n",
    "            preds.iloc[i].T,\n",
    "            y_test_t.iloc[i].T,\n",
    "        ],\n",
    "        axis=1,\n",
    "    )\n",
    "\n",
    "    tmp.columns = [\"predicted\", \"actual\"]\n",
    "\n",
    "    tmp.plot()\n",
    "    plt.ylabel(\"CO concentration\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've seen how to implement the direct forecasting method to perform multistep forecasting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsml",
   "language": "python",
   "name": "fsml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
